3,074 results
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2. 〈Original Papers〉Spatial context learning in change detection task
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visual context ,contextual cueing ,attentional setting ,change detection - Abstract
Visual context, such as a spatial relationship between the locations of a particular target object and the other distractor objects, is learned with repeated presentations of the same spatial layout, and facilitates searching for or detecting the target object. This context learning is referred to as contextual cueing. Previous studies have demonstrated that learned context involved the association target location with individual locations of distractors (nonconfigural learning) and the association target location with the overall distractor configuration (configural learning) in visual search and change detection tasks respectively, indicating that the content of context learning was task specific. The purpose of the present study was to examine whether the content of context learning relied on participants’ attentional control setting during performing change detection task. The results showed that nonconfigural learning tended to occur even in change detection task when it was necessary for detecting the target effectively to direct spatial attention to individual distractors, suggesting the possibility that the aspect of context learning is dependent on participants’ attentional setting., 専攻: 認知心理学
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- 2016
3. A Fast Detection Algorithm for Change Detection in National Forestland "One Map" Based on NLNE Quad-Tree.
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Gao, Fei, Su, Xiaohui, Chen, Yuling, Wu, Baoguo, Tian, Yingze, Zhang, Wenjie, and Li, Tao
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FORESTS & forestry ,FOREST management ,GEOGRAPHIC information systems ,VECTOR data ,MOUNTAIN forests ,ALGORITHMS - Abstract
The National Forestland "One Map" applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in "One Map", taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for "One Map" (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in "One Map" and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review.
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Cheng, Guangliang, Huang, Yunmeng, Li, Xiangtai, Lyu, Shuchang, Xu, Zhaoyang, Zhao, Hongbo, Zhao, Qi, and Xiang, Shiming
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DEEP learning ,FEATURE extraction ,LAND cover ,REMOTE sensing ,AGRICULTURAL surveys ,OPTICAL remote sensing - Abstract
Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task. [ABSTRACT FROM AUTHOR]
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- 2024
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5. TDSCCNet: twin-depthwise separable convolution connect network for change detection with heterogeneous images.
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Min Wang, Liang Huang, Bo-Hui Tang, Weipeng Le, and Qiuyuan Tian
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FEATURE extraction ,OPTICAL sensors ,OPTICAL images ,REMOTE sensing ,DEEP learning - Abstract
The task of change detection (CD) in optical and SAR images is an ever-evolving and demanding subject within the realm of remote sensing (RS). It holds great significance to identify the target areas by using complementary information between the two. Due to the distinct imaging mechanisms employed by optical and SAR sensors, effectively and accurately identifying changing regions can be challenging. To this end, a novel heterogeneous RS images CD network (Twin-Depthwise Separable Convolution Connect Network, TDSCCNet) is proposed in this paper. Image domain transformation is a front-end task, while the back-end employs a single-branch bilayer depthwise separable convolutionconnected encoder-decoder to accomplish CD work. Specifically, first, the cycle-consistent adversarial network (CycleGAN) serves to integrate the optical and SAR visual domains, and a consistent feature expression is obtained. Second, the single-branch encoder structure of bilayer depthwise separable convolution is employed to realize change feature extraction. Finally, the multiscale connected decoder reconstructed by change map is utilized to reconstruct the original images and solve the local discontinuities and holes in the binary change map. Multiscale loss is designated for optimizing the global and local effects to alleviate the class imbalance problem. It was tested on four representative datasets from Gloucester, Shuguang Village, Italy and WV-3 datasets with an overall accuracy of 97.36%, 97.01%, 97.62%, and 98.01% respectively. By comparing the existing methods, experimental results confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
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- 2024
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6. A Review of Crowdsourcing Update Methods for High-Definition Maps.
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Guo, Yuan, Zhou, Jian, Li, Xicheng, Tang, Youchen, and Lv, Zhicheng
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CROWDSOURCING ,GEOGRAPHICAL perception ,DATA mining ,MAPS ,AUTONOMOUS vehicles ,RESEARCH personnel - Abstract
High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Siamese InternImage for Change Detection.
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Shen, Jing, Huo, Chunlei, and Xiang, Shiming
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CONVOLUTIONAL neural networks ,TRANSFORMER models ,REMOTE sensing ,MOTIVATION (Psychology) - Abstract
For some time, CNN was the de facto state-of-the-art method in remote sensing image change detection. Although transformer-based models have surpassed CNN-based models due to their larger receptive fields, CNNs still retain their value for their efficiency and ability to extract precise local features. To overcome the limitations of the restricted receptive fields in standard CNNs, deformable convolution allows for dynamic adjustment of sampling locations in convolutional kernels, improving the network's ability to model global contexts. InternImage is an architecture built upon deformable convolution as its foundational operation. Motivated by InternImage, in this paper, a CNN-based change detection vision foundation model is proposed. By introducing deformable convolution into Siamese InternImage architecture, the proposed CNN-based change detection vision foundation model is capable of capturing long-range dependencies and global information. A refinement block is utilized to merge local detail, where channel attention is incorporated. The proposed approach achieved excellent performance on the LEVIR-CD and WHU-CD datasets. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Iterative Optimization-Enhanced Contrastive Learning for Multimodal Change Detection.
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Tang, Yuqi, Yang, Xin, Han, Te, Sun, Kai, Guo, Yuqiang, and Hu, Jun
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FEATURE extraction ,EMERGENCY management ,REMOTE sensing ,ENVIRONMENTAL management ,ENVIRONMENTAL disasters - Abstract
Multimodal change detection (MCD) harnesses multi-source remote sensing data to identify surface changes, thereby presenting prospects for applications within disaster management and environmental surveillance. Nonetheless, disparities in imaging mechanisms across various modalities impede the direct comparison of multimodal images. In response, numerous methodologies employing deep learning features have emerged to derive comparable features from such images. Nevertheless, several of these approaches depend on manually labeled samples, which are resource-intensive, and their accuracy in distinguishing changed and unchanged regions is not satisfactory. In addressing these challenges, a new MCD method based on iterative optimization-enhanced contrastive learning is proposed in this paper. With the participation of positive and negative samples in contrastive learning, the deep feature extraction network focuses on extracting the initial deep features of multimodal images. The common projection layer unifies the deep features of two images into the same feature space. Then, the iterative optimization module expands the differences between changed and unchanged areas, enhancing the quality of the deep features. The final change map is derived from the similarity measurements of these optimized features. Experiments conducted across four real-world multimodal datasets, benchmarked against eight well-established methodologies, incontrovertibly illustrate the superiority of our proposed approach. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Remote sensing of 50 years of coastal urbanization and environmental change in the Arabian Gulf: a systematic review.
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Dahy, Basam, Al-Memari, Maryam, Al-Gergawi, Amal, and Burt, John A.
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COASTAL zone management ,HABITAT modification ,COASTAL mapping ,CITIES & towns ,MARINE habitats - Abstract
Since the 1970s oil boom, nations surrounding the Arabian Gulf have witnessed rapid coastal urbanization, which accelerated in the early 2000s with the emergence of large-scale coastal 'mega-projects' designed to accommodate growing populations, attract international investments, and promote tourism. This development surge has had profound environmental impacts, including significant habitat modification, land use and land cover (LULC) change, and increased environmental pressure. Remote sensing (RS) technologies have become indispensable tools for monitoring these changes, offering costeffective and non-intrusive methods to map and assess coastal zones. However, RS applications across the Arabian Gulf have been spatially limited, often focusing narrowly on specific cities or habitats while neglecting the broader geographical and coastal dimensions of urbanization. This study addresses this gap by conducting a systematic review of peer-reviewed RS literature from 1971 to 2022, covering the coastal regions of the eight nations bordering the Arabian Gulf. A total of 186 publications were categorized into three focal areas: 1) coastal urbanization and LULC, 2) coastal and marine habitats, and 3) environmental pressures and state changes. The results reveal a significant increase in RS studies in recent years, with around two-thirds of the publications (64.3%) appearing between 2016 and 2022. Studies predominantly focused on environmental pressures and state changes (35%), followed by habitat modification (27%), and coastal urbanization (20%). Geographically, RS research primarily concentrated on the coasts of the southern Gulf (UAE and Qatar) and western Gulf (Bahrain and Saudi Arabia), where major urban centers are located, while the northern Gulf (Kuwait and Iraq) and Iranian coast have been less studied. The systematic review highlights the need for integrated RS and GIS-based monitoring systems that combine different sources of RS data and in situ measurements to evaluate the Gulf as a unified system. Expanding spatial coverage, enhancing temporal analysis, and fostering regional collaboration are necessary to improve the understanding and management of coastal urbanization and environmental changes in the Arabian Gulf. This approach will more effectively inform decision-makers, and support more sustainable coastal management and long-term environmental resilience in the region. [ABSTRACT FROM AUTHOR]
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- 2024
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10. MDFA-Net: Multi-Scale Differential Feature Self-Attention Network for Building Change Detection in Remote Sensing Images.
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Li, Yuanling, Zou, Shengyuan, Zhao, Tianzhong, and Su, Xiaohui
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CONVOLUTIONAL neural networks ,TRANSFORMER models ,FEATURE extraction ,REMOTE sensing ,URBAN studies - Abstract
Building change detection (BCD) from remote sensing images is an essential field for urban studies. In this well-developed field, Convolutional Neural Networks (CNNs) and Transformer have been leveraged to empower BCD models in handling multi-scale information. However, it is still challenging to accurately detect subtle changes using current models, which has been the main bottleneck to improving detection accuracy. In this paper, a multi-scale differential feature self-attention network (MDFA-Net) is proposed to effectively integrate CNN and Transformer by balancing the global receptive field from the self-attention mechanism and the local receptive field from convolutions. In MDFA-Net, two innovative modules were designed. Particularly, a hierarchical multi-scale dilated convolution (HMDConv) module was proposed to extract local features with hybrid dilation convolutions, which can ameliorate the effect of CNN's local bias. In addition, a differential feature self-attention (DFA) module was developed to implement the self-attention mechanism at multi-scale difference feature maps to overcome the problem that local details may be lost in the global receptive field in Transformer. The proposed MDFA-Net achieves state-of-the-art accuracy performance in comparison with related works, e.g., USSFC-Net, in three open datasets: WHU-CD, CDD-CD, and LEVIR-CD. Based on the experimental results, MDFA-Net significantly exceeds other models in F1 score, IoU, and overall accuracy; the F1 score is 93.81%, 95.52%, and 91.21% in WHU-CD, CDD-CD, and LEVIR-CD datasets, respectively. Furthermore, MDFA-Net achieved first or second place in precision and recall in the test in all three datasets, which indicates its better balance in precision and recall than other models. We also found that subtle changes, i.e., small-sized building changes and irregular boundary changes, are better detected thanks to the introduction of HMDConv and DFA. To this end, with its better ability to leverage multi-scale differential information than traditional methods, MDFA-Net provides a novel and effective avenue to integrate CNN and Transformer in BCD. Further studies could focus on improving the model's insensitivity to hyper-parameters and the model's generalizability in practical applications. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Aneurysm growth evaluation and detection: a computer-assisted follow-up MRA analysis.
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Bizjak, Žiga and Špiclin, Žiga
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DISEASE progression ,INTRACRANIAL aneurysms ,ANGIOGRAPHY ,SURFACE area ,INFORMATION resources management - Abstract
Growing intracranial aneurysms pose a high risk of rupture, making the detection and quantification of the growth crucial for timely treatment strategy adoption. In this paper we propose a computer-assisted approach based on the extraction of IA shapes from associated baseline and follow-up angiographic scans and non-rigid morphing of the two shapes. From the obtained shape deformations we computed four novel features, including differential volume (dV), surface area (dSA), aneurysm-size normalized median deformation path length (dMPL), and integral of cumulative deformation distances (dICDD). An experienced neuroradiologist manually extracted the IA shape models from the baseline and follow-up MRAs and, by utilizing size change and visual assessments, classified each aneurysm into stable with morphology changes, stable or growing. We investigated the classification performance and found that three of the novel and one cross-sectional feature exhibited significantly different mean values (p-value < 0.05 ; Tukey's HSD test) between the stable and growing IA groups, while the mean dICDD was significantly different between all the three groups. The cross-sectional features has sensitivity to growing IAs in range 0.05–0.86, while novel features had generally higher sensitivity in range 0.81–0.90, making them promising candidates as surrogate follow-up imaging-based biomarkers for IA growth detection. These findings may offer valuable information for clinical management of patients with IAs based on follow-up imaging. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Flood Mapping of Synthetic Aperture Radar (SAR) Imagery Based on Semi-Automatic Thresholding and Change Detection.
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Lang, Fengkai, Zhu, Yanyin, Zhao, Jinqi, Hu, Xinru, Shi, Hongtao, Zheng, Nanshan, and Zha, Jianfeng
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SYNTHETIC aperture radar ,SPECKLE interference ,URBAN renewal ,WATERSHEDS ,BACKSCATTERING - Abstract
Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]
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- 2024
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13. An Experimental Investigation for Detection, Localization, and Quantification of Compound Changes in Complex Uncertain Systems.
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Abdelbarr, Mohamed H., Hernandez-Garcia, Miguel R., Caffrey, John P., and Masri, Sami F.
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STRUCTURAL health monitoring ,UNCERTAIN systems ,PREDICATE calculus ,LOCALIZATION (Mathematics) ,COMPLEX compounds ,SYSTEM dynamics ,SYSTEM identification ,STRUCTURAL engineering - Abstract
The field of (practical) data-driven approaches that utilize the vibration signature of target systems for developing mathematical models (for computational purposes, control, or anomaly detection for structural health monitoring) is still an active research area, despite the fact that several powerful system identification techniques have been developed in the system dynamics field to analyze such measurements. However, there is still a paucity of comprehensive experimental studies that investigate the range of validity of such identification techniques, particularly those applicable to realistic situations encountered in the structural engineering field, with the focus on detecting, quantifying, locating, and classifying observed changes, especially when there are significant inherent nonlinearities in the reference (undamaged) complex target structure, and where there are unavoidable sources of errors and uncertainties in the measurements and the attendant data analysis procedures. The research team constructed a well-instrumented, reconfigurable test apparatus (resembling a tall building) that allows the introduction of quantifiable levels of composite changes at various locations, orientations, and types of linear and/or nonlinear changes, with the aim of investigating a subset of the aforementioned challenges facing researchers who are interested in assessing the utility of some practical system identification approaches. The primary focus of the identification approach is on a decomposition procedure that is ideally suited for certain types of structures that possess some topological features that can be exploited to enhance the detectability of small changes. A companion paper provides a detailed description of the testbed features and its instrumentation. The present paper focuses on the analysis of some of the very extensive data sets that were created to study the usefulness of some practical dimensionless probabilistic measures that not only provide normalized change indices but also simultaneously attach a confidence level to each of these indices. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy.
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Tian, Huixin, Zhao, Chenning, Xie, Jueping, and Li, Kun
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OPTIMIZATION algorithms ,MANUFACTURING processes ,MATHEMATICAL optimization ,TIME series analysis ,PROBLEM solving - Abstract
As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields. [ABSTRACT FROM AUTHOR]
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- 2024
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15. Land Consumption Classification Using Sentinel 1 Data: A Systematic Review.
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Mastrorosa, Sara, Crespi, Mattia, Congedo, Luca, and Munafò, Michele
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ZONING ,SURFACE of the earth ,SYNTHETIC aperture radar ,LAND cover ,REMOTE sensing - Abstract
The development of remote sensing technology has redefined the approaches to the Earth's surface monitoring. The Copernicus Programme promoted by the European Space Agency (ESA) and the European Union (EU), through the launch of the Synthetic Aperture Radar (SAR) Sentinel-1 and the multispectral Sentinel-2 satellites, has provided a valuable contribution to monitoring the Earth's surface. There are several review articles on the land use/land cover (LULC) matter using Sentinel images, but it lacks a methodical and extensive review in the specific field of land consumption monitoring, concerning the application of SAR images, in particular Sentinel-1 images. In this paper, we explored the potential of Sentinel-1 images to estimate land consumption using mathematical modeling, focusing on innovative approaches. Therefore, this research was structured into three principal steps: (1) searching for appropriate studies, (2) collecting information required from each paper, and (3) discussing and comparing the accuracy of the existing methods to evaluate land consumption and their applied conditions using Sentinel-1 Images. Current research has demonstrated that Sentinel-1 data has the potential for land consumption monitoring around the world, as shown by most of the studies reviewed: the most promising approaches are presented and analyzed. [ABSTRACT FROM AUTHOR]
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- 2023
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16. Global-Local Collaborative Learning Network for Optical Remote Sensing Image Change Detection.
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Li, Jinghui, Shao, Feng, Liu, Qiang, and Meng, Xiangchao
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OPTICAL remote sensing ,COLLABORATIVE learning ,CONVOLUTIONAL neural networks ,TRANSFORMER models ,REMOTE sensing ,ARTIFICIAL satellites - Abstract
Due to the widespread applications of change detection technology in urban change analysis, environmental monitoring, agricultural surveillance, disaster detection, and other domains, the task of change detection has become one of the primary applications of Earth orbit satellite remote sensing data. However, the analysis of dual-temporal change detection (CD) remains a challenge in high-resolution optical remote sensing images due to the complexities in remote sensing images, such as intricate textures, seasonal variations in imaging time, climatic differences, and significant differences in the sizes of various objects. In this paper, we propose a novel U-shaped architecture for change detection. In the encoding stage, a multi-branch feature extraction module is employed by combining CNN and transformer networks to enhance the network's perception capability for objects of varying sizes. Furthermore, a multi-branch aggregation module is utilized to aggregate features from different branches, providing the network with global attention while preserving detailed information. For dual-temporal features, we introduce a spatiotemporal discrepancy perception module to model the context of dual-temporal images. Particularly noteworthy is the construction of channel attention and token attention modules based on the transformer attention mechanism to facilitate information interaction between multi-level features, thereby enhancing the network's contextual awareness. The effectiveness of the proposed network is validated on three public datasets, demonstrating its superior performance over other state-of-the-art methods through qualitative and quantitative experiments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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17. Self-Paced Multi-Scale Joint Feature Mapper for Multi-Objective Change Detection in Heterogeneous Images.
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Wang, Ying, Dang, Kelin, Yang, Rennong, Song, Qi, Li, Hao, and Gong, Maoguo
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PARTICLE swarm optimization ,REMOTE sensing ,LEARNING strategies ,REMOTE-sensing images - Abstract
Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper's capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM's capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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18. Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering.
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Li, Liangliang, Ma, Hongbing, Zhang, Xueyu, Zhao, Xiaobin, Lv, Ming, and Jia, Zhenhong
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PRINCIPAL components analysis ,SYNTHETIC aperture radar ,SYNTHETIC apertures ,CLUSTER analysis (Statistics) ,BUILT environment ,FEATURE extraction - Abstract
Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection. [ABSTRACT FROM AUTHOR]
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- 2024
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19. AMFNet: Attention-Guided Multi-Scale Fusion Network for Bi-Temporal Change Detection in Remote Sensing Images.
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Zhan, Zisen, Ren, Hongjin, Xia, Min, Lin, Haifeng, Wang, Xiaoya, and Li, Xin
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REMOTE sensing ,DEEP learning ,LAND cover ,SUSTAINABLE development ,LAND use ,IMAGE fusion ,REMOTE-sensing images - Abstract
Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model's understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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20. Object-Based Change Detection Algorithm with a Spatial AI Stereo Camera.
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Göncz, Levente and Majdik, András László
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STEREOSCOPIC cameras ,OBJECT recognition (Computer vision) ,STEREO vision (Computer science) ,ARTIFICIAL intelligence ,ARTIFICIAL vision ,ALGORITHMS - Abstract
This paper presents a real-time object-based 3D change detection method that is built around the concept of semantic object maps. The algorithm is able to maintain an object-oriented metric-semantic map of the environment and can detect object-level changes between consecutive patrol routes. The proposed 3D change detection method exploits the capabilities of the novel ZED 2 stereo camera, which integrates stereo vision and artificial intelligence (AI) to enable the development of spatial AI applications. To design the change detection algorithm and set its parameters, an extensive evaluation of the ZED 2 camera was carried out with respect to depth accuracy and consistency, visual tracking and relocalization accuracy and object detection performance. The outcomes of these findings are reported in the paper. Moreover, the utility of the proposed object-based 3D change detection is shown in real-world indoor and outdoor experiments. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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21. HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land.
- Author
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Li, Fangting, Zhou, Fangdong, Zhang, Guo, Xiao, Jianfeng, and Zeng, Peng
- Subjects
AGRICULTURE ,LAND resource ,REMOTE-sensing images ,SUSTAINABLE development ,LAND use planning - Abstract
Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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22. MFINet: Multi-Scale Feature Interaction Network for Change Detection of High-Resolution Remote Sensing Images.
- Author
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Ren, Wuxu, Wang, Zhongchen, Xia, Min, and Lin, Haifeng
- Subjects
REMOTE sensing ,TRANSFORMER models ,INFORMATION networks ,OPTICAL remote sensing ,REMOTE-sensing images - Abstract
Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing methods have not fully tapped the potential of multi-scale bi-temporal features to interact layer by layer. Therefore, this paper proposes a multi-scale feature interaction network (MFINet). The network realizes the information interaction of multi-temporal images by inserting a bi-temporal feature interaction layer (BFIL) between backbone networks at the same level, guides the attention to focus on the difference region, and suppresses the interference. At the same time, a double temporal feature fusion layer (BFFL) is used at the end of the coding layer to extract subtle difference features. By introducing the transformer decoding layer and improving the recovery effect of the feature size, the ability of the network to accurately capture the details and contour information of the building is further improved. The F1 of our model on the public dataset LEVIR-CD reaches 90.12%, which shows better accuracy and generalization performance than many state-of-the-art change detection models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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23. A Renovated Framework of a Convolution Neural Network with Transformer for Detecting Surface Changes from High-Resolution Remote-Sensing Images.
- Author
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Yao, Shunyu, Wang, Han, Su, Yalu, Li, Qing, Sun, Tao, Liu, Changjun, Li, Yao, and Cheng, Deqiang
- Subjects
CONVOLUTIONAL neural networks ,TRANSFORMER models ,SURFACE of the earth ,FEATURE extraction ,REMOTE sensing - Abstract
Natural hazards are considered to have a strong link with climate change and human activities. With the rapid advancements in remote sensing technology, real-time monitoring and high-resolution remote-sensing images have become increasingly available, which provide precise details about the Earth's surface and enable prompt updates to support risk identification and management. This paper proposes a new network framework with Transformer architecture and a Residual network for detecting the changes in high-resolution remote-sensing images. The proposed model is trained using remote-sensing images from Shandong and Anhui Provinces of China in 2021 and 2022 while one district in 2023 is used to test the prediction accuracy. The performance of the proposed model is evaluated by using five matrices and further compared to both convention-based and attention-based models. The results demonstrated that the proposed structure integrates the great capability of conventional neural networks for image feature extraction with the ability to obtain global context from the attention mechanism, resulting in significant improvements in balancing positive sample identification while avoiding false positives in complex image change detection. Additionally, a toolkit supporting image preprocessing is developed for practical applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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24. Object Identification in Land Parcels Using a Machine Learning Approach.
- Author
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Gundermann, Niels, Löwe, Welf, Fransson, Johan E. S., Olofsson, Erika, and Wehrenpfennig, Andreas
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MACHINE learning ,CONVOLUTIONAL neural networks ,IMAGE recognition (Computer vision) ,ARTIFICIAL intelligence ,LAND use - Abstract
This paper introduces an AI-based approach to detect human-made objects and changes in these on land parcels. To this end, we used binary image classification performed by a convolutional neural network. Binary classification requires the selection of a decision boundary, and we provided a deterministic method for this selection. Furthermore, we varied different parameters to improve the performance of our approach, leading to a true positive rate of 91.3% and a true negative rate of 63.0%. A specific application of our work supports the administration of agricultural land parcels eligible for subsidiaries. As a result of our findings, authorities could reduce the effort involved in the detection of human made changes by approximately 50%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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25. Multistage Interaction Network for Remote Sensing Change Detection.
- Author
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Zhou, Meng, Qian, Weixian, and Ren, Kan
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DATA mining ,FEATURE extraction ,DEEP learning - Abstract
Change detection in remote sensing imagery is vital for Earth monitoring but faces challenges such as background complexity and pseudo-changes. Effective interaction between bitemporal images is crucial for accurate change information extraction. This paper presents a multistage interaction network designed for effective change detection, incorporating interaction at the image, feature, and decision levels. At the image level, change information is directly extracted from intensity changes, mitigating potential change information loss during feature extraction. Instead of separately extracting features from bitemporal images, the feature-level interaction jointly extracts features from bitemporal images. By enhancing relevance to spatial variant information and shared semantic channels, the network excels in overcoming background complexity and pseudo-changes. The decision-level interaction combines image-level and feature-level interactions, producing multiscale feature differences for precise change prediction. Extensive experiments demonstrate the superior performance of our method compared to existing approaches, establishing it as a robust solution for remote sensing image change detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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26. A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning.
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Wang, Zhibao, Zhang, Jie, Bai, Lu, Chang, Huan, Chen, Yuanlin, Zhang, Ying, and Tao, Jinhua
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ACTIVE learning ,CROWDSOURCING ,DEEP learning ,FORESTS & forestry ,ARTIFICIAL intelligence ,LAND cover ,REMOTE sensing ,REMOTE-sensing images - Abstract
Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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27. A Siamese Swin-Unet for image change detection.
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Tang, Yizhuo, Cao, Zhengtao, Guo, Ningbo, and Jiang, Mingyong
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CONVOLUTIONAL neural networks ,TRANSFORMER models ,DEEP learning ,AGRICULTURAL forecasts ,LAND resource ,REMOTE sensing ,REMOTE-sensing images - Abstract
The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying). [ABSTRACT FROM AUTHOR]
- Published
- 2024
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28. Improving Unsupervised Object-Based Change Detection via Hierarchical Multi-Scale Binary Partition Tree Segmentation: A Case Study in the Yellow River Source Region.
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Du, Yihong, He, Xiaoming, Chen, Liujia, Wang, Duo, Jiao, Weili, Liu, Yongkun, He, Guojin, and Long, Tengfei
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ECOSYSTEM dynamics ,REMOTE sensing ,ECOLOGICAL disturbances ,CONSTRAINT algorithms ,AIRBORNE lasers ,IMAGE processing - Abstract
Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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29. High-Resolution Remote Sensing Image Change Detection Based on Cross-Mixing Attention Network.
- Author
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Wu, Chaoyang, Yang, Le, Guo, Cunge, and Wu, Xiaosuo
- Subjects
REMOTE sensing ,CONVOLUTIONAL neural networks ,FEATURE selection ,REMOTE-sensing images ,OPTIMIZATION algorithms - Abstract
With the powerful discriminative capabilities of convolutional neural networks, change detection has achieved significant success. However, current methods either ignore the spatiotemporal dependencies between dual-temporal images or suffer from decreased accuracy due to registration errors. Addressing these challenges, this paper proposes a method for remote sensing image change detection based on the cross-mixing attention network. To minimize the impact of registration errors on change detection results, a feature alignment module (FAM) is specifically developed in this study. The FAM performs spatial transformations on dual-temporal feature maps, achieving the precise spatial alignment of feature pairs and reducing false positive rates in change detection. Additionally, to fully exploit the spatiotemporal relationships between dual-temporal images, a cross-mixing attention module (CMAM) is utilized to extract global channel information, enhancing feature selection capabilities. Furthermore, attentional maps are created to guide the up-sampling process, optimizing feature information. Comprehensive experiments conducted on the LEVIR-CD and SYSU-CD change detection datasets demonstrate that the proposed model achieves F1 scores of 91.06% and 81.88%, respectively, outperforming other comparative models. In conclusion, the proposed model maintains good performance on two datasets and, thus, has good applicability in various change detection tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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30. A review of multi-class change detection for satellite remote sensing imagery.
- Author
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Zhu, Qiqi, Guo, Xi, Li, Ziqi, and Li, Deren
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THEMATIC mapper satellite ,LANDSAT satellites ,URBAN growth ,ARTIFICIAL satellites ,ENVIRONMENTAL research ,ENVIRONMENTAL monitoring ,REMOTE sensing ,RESEARCH personnel ,OPTICAL remote sensing - Abstract
Change Detection (CD) provides a research basis for environmental monitoring, urban expansion and reconstruction as well as disaster assessment, by identifying the changes of ground objects in different time periods. Traditional CD focused on the Binary Change Detection (BCD), focusing solely on the change and no-change regions. Due to the dynamic progress of earth observation satellite techniques, the spatial resolution of remote sensing images continues to increase, Multi-class Change Detection (MCD) which can reflect more detailed land change has become a hot research direction in the field of CD. Although many scholars have reviewed change detection at present, most of the work still focuses on BCD. This paper focuses on the recent progress in MCD, which includes five major aspects: challenges, datasets, methods, applications and future research direction. Specifically, the background of MCD is first introduced. Then, the major difficulties and challenges in MCD are discussed and delineated. The benchmark datasets for MCD are described, and the available open datasets are listed. Moreover, MCD is further divided into three categories and the specific techniques are described, respectively. Subsequently, the common applications of MCD are described. Finally, the relevant literature in the main journals of remote sensing in the past five years are analyzed and the development and future research direction of MCD are discussed. This review will help researchers understand this field and provide a reference for the subsequent development of MCD. Our collections of MCD benchmark datasets are available at: [ABSTRACT FROM AUTHOR]
- Published
- 2024
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31. MSGFNet: Multi-Scale Gated Fusion Network for Remote Sensing Image Change Detection.
- Author
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Wang, Yukun, Wang, Mengmeng, Hao, Zhonghu, Wang, Qiang, Wang, Qianwen, and Ye, Yuanxin
- Subjects
REMOTE sensing ,DEEP learning ,REMOTE-sensing images - Abstract
Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain as most CD methods suffer from ineffective feature fusion. Therefore, this paper presents a multi-scale gated fusion network (MSGFNet) to improve the accuracy of CD results. To effectively extract bi-temporal features, the EfficientNetB4 model based on a Siamese network is employed. Subsequently, we propose a multi-scale gated fusion module (MSGFM) that comprises a multi-scale progressive fusion (MSPF) unit and a gated weight adaptive fusion (GWAF) unit, aimed at fusing bi-temporal multi-scale features to maintain boundary details and detect completely changed targets. Finally, we use the simple yet efficient UNet structure to recover the feature maps and predict results. To demonstrate the effectiveness of the MSGFNet, the LEVIR-CD, WHU-CD, and SYSU-CD datasets were utilized, and the MSGFNet achieved F1 scores of 90.86%, 92.46%, and 80.39% on the three datasets, respectively. Furthermore, the low computational costs and small model size have validated the superior performance of the MSGFNet. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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32. A Multi-Scale Graph Based on Spatio-Temporal-Radiometric Interaction for SAR Image Change Detection.
- Author
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Zhang, Peijing, Jiang, Jinbao, Kou, Peng, Wang, Shining, and Wang, Bin
- Subjects
SPECKLE interference ,MARKOV random fields ,SYNTHETIC aperture radar ,LAND use ,K-nearest neighbor classification ,ECOSYSTEM services - Abstract
Change detection (CD) in remote sensing imagery has found broad applications in ecosystem service assessment, disaster evaluation, urban planning, land utilization, etc. In this paper, we propose a novel graph model-based method for synthetic aperture radar (SAR) image CD. To mitigate the influence of speckle noise on SAR image CD, we opt for comparing the structures of multi-temporal images instead of the conventional approach of directly comparing pixel values, which is more robust to the speckle noise. Specifically, we first segment the multi-temporal images into square patches at multiple scales and construct multi-scale K-nearest neighbor (KNN) graphs for each image, and then develop an effective graph fusion strategy, facilitating the exploitation of multi-scale information within SAR images, which offers an enhanced representation of the complex relationships among features in the images. Second, we accomplish the interaction of spatio-temporal-radiometric information between graph models through graph mapping, which can efficiently uncover the connections between multi-temporal images, leading to a more precise extraction of changes between the images. Finally, we use the Markov random field (MRF) based segmentation method to obtain the binary change map. Through extensive experimentation on real datasets, we demonstrate the remarkable superiority of our methodologies by comparing with some current state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
33. Multi-Scale Feature Interaction Network for Remote Sensing Change Detection.
- Author
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Zhang, Chong, Zhang, Yonghong, and Lin, Haifeng
- Subjects
REMOTE sensing ,PIXELS ,LINEAR network coding ,FEATURE extraction ,INDUSTRIAL costs ,DATA analysis - Abstract
Change detection (CD) is an important remote sensing (RS) data analysis technology. Existing remote sensing change detection (RS-CD) technologies cannot fully consider situations where pixels between bitemporal images do not correspond well on a one-to-one basis due to factors such as seasonal changes and lighting conditions. Existing networks construct two identical feature extraction branches through convolution, which share weights. The two branches work independently and do not merge until the feature mapping is sent to the decoder head. This results in a lack of feature information interaction between the two images. So, directing attention to the change area is of research interest. In complex backgrounds, the loss of edge details is very important. Therefore, this paper proposes a new CD algorithm that extracts multi-scale feature information through the backbone network in the coding stage. According to the task characteristics of CD, two submodules (the Feature Interaction Module and Detail Feature Guidance Module) are designed to make the feature information between the bitemporal RS images fully interact. Thus, the edge details are restored to the greatest extent while fully paying attention to the change areas. Finally, in the decoding stage, the feature information of different levels is fully used for fusion and decoding operations. We build a new CD dataset to further verify and test the model's performance. The generalization and robustness of the model are further verified by using two open datasets. However, due to the relatively simple construction of the model, it cannot handle the task of multi-classification CD well. Therefore, further research on multi-classification CD algorithms is recommended. Moreover, due to the high production cost of CD datasets and the difficulty in obtaining them in practical tasks, future research will look into semi-supervised or unsupervised related CD algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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34. Sparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection.
- Author
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Erturk, Alp, Iordache, Marian-Daniel, and Plaza, Antonio
- Abstract
The localization of changes that occur between the images in a multitemporal series is crucial for many applications, ranging from environmental monitoring to military surveillance. In contrast to traditional change detection methods, unmixing-based change detection has been shown to have the important added benefit of providing subpixel-level information on the nature of the changes, instead of only providing the location of the changes. Recently, sparse unmixing has also been introduced to hyperspectral change detection, resulting in a method that circumvents the drawbacks of regular spectral unmixing approaches. Sparse unmixing-based change detection reveals the changes that occur in a multitemporal series, at subpixel level, and in terms of the library spectra and their sparse abundances, and provides enhanced change detection performance, especially when subpixel-level changes have occurred. However, sparse unmixing is generally an ill-conditioned and time-consuming process, especially as the size of the utilized spectral library increases. In this paper, dictionary pruning is exploited for the first time for hyperspectral change detection using sparse unmixing, in order to alleviate the ill-conditioning of the problem and achieve decreased computation times and enhanced change detection performance. Experimental results on both realistic synthetic and real datasets are used to validate the proposed approach. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
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35. Learning Relationship for Very High Resolution Image Change Detection
- Author
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Chunlei Huo, Chunhong Pan, Kun Ding, Keming Chen, and Zhixin Zhou
- Subjects
Very high resolution ,Atmospheric Science ,Computer science ,business.industry ,Feature extraction ,0211 other engineering and technologies ,Relationship learning ,Pattern recognition ,02 engineering and technology ,Paper based ,Machine learning ,computer.software_genre ,Support vector machine ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Computers in Earth Sciences ,business ,Image resolution ,Classifier (UML) ,computer ,Change detection ,021101 geological & geomatics engineering - Abstract
The difficulty of very high resolution image change detection lies in the low interclass separability between the changed class and the unchanged class. According to experiments, we found that this separability can be improved by mining the relationship contained in the training samples. Based on this observation, a supervised change detection approach is proposed in this paper based on relationship learning. The proposed approach begins with enriching the training samples based on their neighborhood relationship and label coherence; this relationship is then learned simultaneously with the classifier, and, finally, the latter classification performance benefits from the learned relationship. Experiments demonstrate the effectiveness of the proposed approach.
- Published
- 2016
36. MFNet: Mutual Feature-Aware Networks for Remote Sensing Change Detection.
- Author
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Zhang, Qi, Lu, Yao, Shao, Sicheng, Shen, Li, Wang, Fei, and Zhang, Xuetao
- Subjects
ARTIFICIAL neural networks ,FEATURE selection ,PIXELS ,FEATURE extraction ,DEEP learning ,PROBLEM solving ,REMOTE sensing ,EDGE detection (Image processing) - Abstract
Remote sensing change detection involves detecting pixels that have changed from a bi-temporal image of the same location. Current mainstream change detection models use encoder-decoder structures as well as Siamese networks. However, there are still some challenges with this: (1) Existing change feature fusion approaches do not take into account the symmetry of change features, which leads to information loss; (2) The encoder is independent of the change detection task, and feature extraction is performed separately for dual-time images, which leads to underutilization of the encoder parameters; (3) There are problems of unbalanced positive and negative samples and bad edge region detection. To solve the above problems, a mutual feature-aware network (MFNet) is proposed in this paper. Three modules are proposed for the purpose: (1) A symmetric change feature fusion module (SCFM), which uses double-branch feature selection without losing feature information and focuses explicitly on focal spatial regions based on cosine similarity to introduce strong a priori information; (2) A mutual feature-aware module (MFAM), which introduces change features in advance at the encoder stage and uses a cross-type attention mechanism for long-range dependence modeling; (3) A loss function for edge regions. After detailed experiments, the F1 scores of MFNet on SYSU-CD and LEVIR-CD were 83.11% and 91.52%, respectively, outperforming several advanced algorithms, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Prior Semantic Information Guided Change Detection Method for Bi-temporal High-Resolution Remote Sensing Images.
- Author
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Pang, Shiyan, Li, Xinyu, Chen, Jia, Zuo, Zhiqi, and Hu, Xiangyun
- Subjects
REMOTE sensing ,DEEP learning ,LAND resource ,LAND cover ,CONVOLUTIONAL neural networks ,NATURAL disasters - Abstract
High-resolution remote sensing image change detection technology compares and analyzes bi-temporal or multitemporal high-resolution remote sensing images to determine the change areas. It plays an important role in land cover/use monitoring, natural disaster monitoring, illegal building investigation, military target strike effect analysis, and land and resource investigation. The change detection of high-resolution remote sensing images has developed rapidly from data accumulation to algorithm models because of the rapid development of technologies such as deep learning and earth observation in recent years. However, the current deep learning-based change detection methods are strongly dependent on large sample data, and the training model has insufficient cross-domain generalization ability. As a result, a prior semantic information-guided change detection framework (PSI-CD), which alleviates the change detection model's dependence on datasets by making full use of prior semantic information, is proposed in this paper. The proposed method mainly includes two parts: one is a prior semantic information generation network that uses the semantic segmentation dataset to extract robust and reliable prior semantic information; the other is the prior semantic information guided change detection network that makes full use of prior semantic information to reduce the sample size of the change detection. To verify the effectiveness of the proposed method, we produced pixel-level semantic labels for the bi-temporal images of the public change detection dataset (LEVIR-CD). Then, we performed extensive experiments on the WHU and LEVIR-CD datasets, including comparisons with existing methods, experiments with different amounts of data, and ablation study, to show the effectiveness of the proposed method. Compared with other existing methods, our method has the highest IoU for all training samples and different amounts of training samples on WHU and LEVIR-CD, reaching a maximum of 83.25% and 83.80%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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38. SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY
- Author
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İnanç Taş, Abdullah E. Akay, B. Gencal, and Akay, Abdullah Emin
- Subjects
lcsh:Applied optics. Photonics ,Hydrology ,geography ,geography.geographical_feature_category ,Contextual image classification ,Land use ,lcsh:T ,Short paper ,lcsh:TA1501-1820 ,Image processing ,Forestry ,Land cover ,Land use/Land cover ,lcsh:Technology ,Swamp ,Protected areas ,lcsh:TA1-2040 ,Band selection ,Spatiotemporal change ,Environmental science ,lcsh:Engineering (General). Civil engineering (General) ,Change detection ,Karacabey flooded forest - Abstract
4th International Workshop on Geoinformation Science / 4th ISPRS International Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling (GeoAdvances) -- OCT 14-15, 2017 -- Karabuk Univ, Safranbolu Campus, Safranbolu, TURKEY Akay, Abdullah Emin/0000-0001-6558-9029 WOS:000568997100006 This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased. Int Soc Photogrammetry & Remote Sensing
- Published
- 2018
39. The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives.
- Author
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Gu, Zhujun and Zeng, Maimai
- Abstract
The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
40. A Cross-Domain Change Detection Network Based on Instance Normalization.
- Author
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Song, Yabin, Xiang, Jun, Jiang, Jiawei, Yan, Enping, Wei, Wei, and Mo, Dengkui
- Subjects
FOREST monitoring ,NATURAL disasters ,LAND resource ,REMOTE sensing - Abstract
Change detection is a crucial task in remote sensing that finds broad application in land resource planning, forest resource monitoring, natural disaster monitoring, and evaluation. In this paper, we propose a change detection model for cross-domain recognition, which we call CrossCDNet. Our model significantly improves the modeling ability of the change detection on one dataset and demonstrates good generalization on another dataset without any additional operations. To achieve this, we employ a Siamese neural network for change detection and design an IBNM (Instance Normalization and Batch Normalization Module) that utilizes instance normalization and batch normalization in order to serve as the encoder backbone in the Siamese neural network. The IBNM extracts feature maps for each layer, and the Siamese neural network fuses the feature maps of the two branches using a unique operation. Finally, a simple MLP decoder is used for end-to-end change detection. We train our model on the LEVIR-CD dataset and achieve competitive performance on the test set. In cross-domain dataset testing, CrossCDNet outperforms all the other compared models. Specifically, our model achieves an F1-score of 91.69% on the LEVIR-CD dataset and an F1-score of 77.09% on the WHU-CD dataset, where the training set was LEVIR-CD. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
41. A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery.
- Author
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Murdaca, Gianluca, Ricciuti, Federico, Rucci, Alessio, Le Saux, Bertrand, Fumagalli, Alfio, and Prati, Claudio
- Subjects
DEEP learning ,SUPERVISED learning ,STRIP mining ,SYNTHETIC aperture radar ,DATA augmentation ,CLOUDINESS ,RANGELANDS - Abstract
Detecting and monitoring changes in open-pit mines is crucial for efficient mining operations. Indeed, these changes comprise a broad spectrum of activities that can often lead to significant environmental impacts such as surface damage, air pollution, soil erosion, and ecosystem degradation. Conventional optical sensors face limitations due to cloud cover, hindering accurate observation of the mining area. To overcome this challenge, synthetic aperture radar (SAR) images have emerged as a powerful solution, due to their unique ability to penetrate clouds and provide a clear view of the ground. The open-pit mine change detection task presents significant challenges, justifying the need for a model trained for this specific task. First, different mining areas frequently include various features, resulting in a diverse range of land cover types within a single scene. This heterogeneity complicates the detection and distinction of changes within open-pit mines. Second, pseudo changes, e.g., equipment movements or humidity fluctuations, which show statistically reliable reflectivity changes, lead to false positives, as they do not directly correspond to the actual changes of interest, i.e., blasting, collapsing, or waste pile operations. In this paper, to the best of our knowledge, we present the first deep learning model in the literature that can accurately detect changes within open-pit mines using SAR images (TerraSAR-X). We showcase the fundamental role of data augmentations and a coherence layer as a critical component in enhancing the model's performance, which initially relied solely on amplitude information. In addition, we demonstrate how, in the presence of a few labels, a pseudo-labeling pipeline can improve the model robustness, without degrading the performance by introducing misclassification points related to pseudo changes. The F1-Score results show that our deep learning approach is a reliable and effective method for SAR change detection in the open-pit mining sector. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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42. Change detection using multi-scale convolutional feature maps of bi-temporal satellite high-resolution images.
- Author
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Alshehhi, Rasha and Marpu, Prashanth R.
- Subjects
REMOTE-sensing images ,URBAN agriculture ,REMOTE sensing ,ERROR functions ,OIL spills - Abstract
Change detection in high-resolution satellite images is essential to understanding the land surface (e.g. agriculture and urban change) or maritime surface (e.g. oil spilling). Many deep-learning-based change detection methods have been proposed to enhance the performance of the classical techniques. However, the massive amount of satellite images and missing ground-truth images are still challenging concerns. In this paper, we propose a supervised deep network for change detection in bi-temporal remote sensing images. We feed multi-level features from convolutional networks of two images (feature-extraction) into one architecture (feature-difference) to have better shape and texture properties using a dual attention module We also utilize a multi-scale dice coefficient error function to decrease overlapping between changed and background pixel. The network is applied to public datasets (ACD, SYSU-CD and OSCD). We compare the proposed architecture with various attention modules and loss functions to verfiy the performance of the proposed method. We also compare the proposed method with the stateof-the-art methods in terms of three metrics: precision, recall and F1-score. The experimental outcomes confirm that the proposed method has good performance compared to benchmark methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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43. Spaceborne SAR Time-Series Images Change Detection Based on SAR-SIFT-Logarithm Background Subtraction.
- Author
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Shen, Wenjie, Jia, Yunzhen, Wang, Yanping, Lin, Yun, Li, Yang, Bai, Zechao, and Jiang, Wen
- Subjects
CHANGE-point problems ,SYNTHETIC aperture radar ,IMAGE registration ,URBAN planning ,ENVIRONMENTAL monitoring ,TIME series analysis - Abstract
Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically adopt a pairwise comparison strategy to obtain multi-temporal change information. However, this kind of method has the problem of losing the overall change information, which is time consuming. To address this problem, this paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction. This algorithm combines the SAR-SIFT image registration technology with Logarithm Background Subtraction. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images will be coregistered by the SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the parts that remained unchanged throughout the time period are modeled with a median filter to obtain the static background. The change information is then obtained via the subtraction of background and CFAR detection and clustering. The proposed algorithm is validated using the Sentinel-1 GRD and PAZ-1 time-series dataset. Experimental results demonstrate that the proposed method effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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44. Sensitivity to temporal structure facilitates perceptual analysis of complex auditory scenes
- Author
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Samantha Picken, Maria Chait, Lefkothea-Vasiliki Andreou, and Lucie Aman
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Predictive coding ,0301 basic medicine ,Soundscape ,Auditory scene analysis ,Computer science ,media_common.quotation_subject ,Speech recognition ,Time perception ,Context (language use) ,03 medical and health sciences ,0302 clinical medicine ,Hearing ,Perception ,Attention ,Sensitivity (control systems) ,media_common ,Structure (mathematical logic) ,Temporal regularity ,Sensory Systems ,030104 developmental biology ,Acoustic Stimulation ,Auditory Perception ,Change detection ,Change deafness ,030217 neurology & neurosurgery ,Research Paper - Abstract
Highlights • Perception relies on sensitivity to predictable structure in the environment. • We used artificial acoustic scenes to investigate this in the auditory modality. • Listeners track the temporal structure of multiple concurrent acoustic streams. • Sensitivity to predictable structure supports auditory scene analysis, even when scenes are complex. • Benefit of regularity observed even when listeners are unaware of the predictable structure., The notion that sensitivity to the statistical structure of the environment is pivotal to perception has recently garnered considerable attention. Here we investigated this issue in the context of hearing. Building on previous work (Sohoglu and Chait, 2016a; elife), stimuli were artificial ‘soundscapes’ populated by multiple (up to 14) simultaneous streams (‘auditory objects’) comprised of tone-pip sequences, each with a distinct frequency and pattern of amplitude modulation. Sequences were either temporally regular or random. We show that listeners’ ability to detect abrupt appearance or disappearance of a stream is facilitated when scene streams were characterized by a temporally regular fluctuation pattern. The regularity of the changing stream as well as that of the background (non-changing) streams contribute independently to this effect. Remarkably, listeners benefit from regularity even when they are not consciously aware of it. These findings establish that perception of complex acoustic scenes relies on the availability of detailed representations of the regularities automatically extracted from multiple concurrent streams.
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- 2021
45. Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection.
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Liu, Lirong, Tang, Xinming, Gan, Yuhang, You, Shucheng, Luo, Zhengyu, Du, Lei, and He, Yun
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FEATURE extraction ,REMOTE sensing ,PROCESS optimization ,DATA mining ,CONVOLUTIONAL neural networks - Abstract
To meet the demands of natural resource monitoring, land development supervision, and other applications for high-precision and high-frequency information extraction from constructed land change, this paper focused on automatic feature extraction and data processing optimization methods for newly constructed bare land based on remote sensing images. A generalized deep convolutional neural network change detection model framework integrating multi-scale information was developed for the automatic extraction of change information. To resolve the problems in the automatic extraction of new bare land parcels, such as mis-extractions and parcel fragmentation, a proximity evaluation model that integrates the confidence-based semantic distance and spatial distance between parcels and their overlapping area is proposed to perform parcel aggregation. Additionally, we propose a complete set of optimized processing techniques from pixel pre-processing to vector post-processing. The results demonstrated that the aggregation method developed in this study is more targeted and effective than ArcGIS for the automatically extracted land change parcels. Additionally, compared with the initial parcels, the total number of optimized parcels decreased by more than 50% and the false detection rate decreased by approximately 30%. These results indicate that this method can markedly reduce the overall data volume and false detection rate of automatically extracted parcels through post-processing under certain conditions of the model and samples and provide technical support for applying the results of automatic feature extraction in engineering practices. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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46. Global Context-Enhanced Network for Pixel-Level Change Detection in Remote Sensing Images.
- Author
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Zixue Zhao, Zhengpeng Li, Jiawei Miao, Kunyang Wu, and Jiansheng Wu
- Subjects
REMOTE sensing ,TRANSFORMER models ,DEEP learning ,COMPUTATIONAL complexity ,CLASSIFICATION - Abstract
Despite the ongoing advancements in deep learning, challenges persist in the domain of change detection in remote sensing imagery. Objects with intricate structures and features may exhibit different shapes or appearances at different times or spatial locations. While most models aim to improve the performance of change detection tasks, these enhancements may lead to significantly increased computational efficiency. In this paper, we propose a global context enhancement network. Firstly, we use ResNet18 to extract dual-temporal features, which are then represented as concise semantic labels by an image semantic extractor. Subsequently, we process these semantic labels through a contextual transformer encoder to generate more refined remote sensing semantic labels enriched with abundant contextual information. The refined semantic labels are integrated with the original features and processed through a Transformer decoder to generate enhanced dual-temporal feature maps. Finally, through the processing of the classification head, we obtain pixel-level predictive images. Extensive experiments conducted on two public change detection datasets yielded impressive results, achieving an F1 score of 89.95% on the WHU-CD dataset and 95.16% on the SVCD dataset. When compared to state-of-the-art change detection models, our approach not only achieves significant performance gains but also maintains relatively high computational efficiency. Our method excels in capturing relevant features and relationships within input data, thereby enhancing the model's ability to represent relationships between different features. This results in a significant performance improvement without adding to the computational complexity. [ABSTRACT FROM AUTHOR]
- Published
- 2024
47. Remote Sensing Image-Change Detection with Pre-Generation of Depthwise-Separable Change-Salient Maps.
- Author
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Li, Bin, Wang, Guanghui, Zhang, Tao, Yang, Huachao, and Zhang, Shubi
- Subjects
REMOTE sensing ,DEEP learning - Abstract
Remote sensing change detection (CD) identifies changes in each pixel of certain classes of interest from a set of aligned image pairs. It is challenging to accurately identify natural changes in feature categories due to unstructured and temporal changes. This research proposed an effective bi-temporal remote sensing CD comprising an encoder that could extract multiscale features, a decoder that focused on semantic alignment between temporal features, and a classification head. In the decoder, we constructed a new convolutional attention structure based on pre-generation of depthwise-separable change-salient maps (PDACN) that could reduce the attention of the network on unchanged regions and thus reduce the potential pseudo-variation in the data sources caused by semantic differences in illumination and subtle alignment differences. To demonstrate the effectiveness of the PDA attention structure, we designed a lightweight network structure for encoders under both convolution-based and transformer architectures. The experiments were conducted on a single-building CD dataset (LEVIR-CD) and a more complex multivariate change type dataset (SYSU-CD). The results showed that our PDA attention structure generated more discriminative change variance information while the entire network model obtained the best performance results with the same level of network model parameters in the transformer architecture. For LEVIR-CD, we achieved an intersection over union (IoU) of 0.8492 and an F1 score of 0.9185. For SYSU-CD, we obtained an IoU of 0.7028 and an F1 score of 0.8255. The experimental results showed that the method proposed in this paper was superior to some current state-of-the-art CD methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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48. Monitoring Urban Growth and Change Detection in Built-up Areas with High-Resolution Satellite Imageries.
- Author
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Abas, Yousf A., Mostafa, Yasser G., BESHEER, Mohamed A., and Farrag, Mohamed F. A.
- Subjects
URBAN growth ,LANDSAT satellites ,REMOTE sensing ,REMOTE-sensing images ,FARMS ,IMAGE processing - Abstract
Copyright of Journal of Engineering Sciences is the property of Faculty of Engineering - Assiut University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
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49. Effects on Long-Range Dependence and Multifractality in Temporal Resolution Recovery of High Frame Rate HEVC Compressed Content.
- Author
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Gavrovska, Ana
- Subjects
VIDEO compression ,VIDEO coding ,K-nearest neighbor classification ,MULTIFRACTALS ,PHOTON upconversion - Abstract
In recent years, video research has dealt with high-frame-rate (HFR) content. Even though low or standard frame rates (SFR) that correspond to values less than 60 frames per second (fps) are still covered. Temporal conversions are applied accompanied with video compression and, thus, it is of importance to observe and detect possible effects of typical compressed video manipulations over HFR (60 fps+) content. This paper addresses ultra-high-definition HFR content via Hurst index as a measure of long-range dependency (LRD), as well as using Legendre multifractal spectrum, having in mind standard high-efficiency video coding (HEVC) format and temporal resolution recovery (TRR), meaning frame upconversion after temporal filtering of compressed content. LRD and multifractals-based studies using video traces have been performed for characterization of compressed video, and they are mostly presented for advanced video coding (AVC). Moreover, recent studies have shown that it is possible to perform TRR detection for SFR data compressed with standards developed before HEVC. In order to address HEVC HFR data, video traces are analyzed using LRD and multifractals, and a novel TRR detection model is proposed based on a weighted k-nearest neighbors (WkNN) classifier and multifractals. Firstly, HFR video traces are gathered using six constant rate factors (crfs), where Hurst indices and multifractal spectra are calculated. According to TRR and original spectra comparison, a novel detection model is proposed based on new multifractal features. Also, five-fold cross-validation using the proposed TRR detection model gave high-accuracy results of around 98%. The obtained results show the effects on LRD and multifractality and their significance in understanding changes in typical video manipulation. The proposed model can be valuable in video credibility and quality assessments of HFR HEVC compressed content. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. D2ANet: Difference-aware attention network for multi-level change detection from satellite imagery.
- Author
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Mei, Jie, Zheng, Yi-Bo, and Cheng, Ming-Ming
- Subjects
REMOTE-sensing images ,LANDSAT satellites ,EMERGENCY management ,NATURAL disasters ,SOURCE code ,GLOBAL method of teaching - Abstract
Recognizing dynamic variations on the ground, especially changes caused by various natural disasters, is critical for assessing the severity of the damage and directing the disaster response. However, current workflows for disaster assessment usually require human analysts to observe and identify damaged buildings, which is labor-intensive and unsuitable for large-scale disaster areas. In this paper, we propose a difference-aware attention network (D2ANet) for simultaneous building localization and multi-level change detection from the dual-temporal satellite imagery. Considering the differences in different channels in the features of pre- and post-disaster images, we develop a dual-temporal aggregation module using paired features to excite change-sensitive channels of the features and learn the global change pattern. Since the nature of building damage caused by disasters is diverse in complex environments, we design a difference-attention module to exploit local correlations among the multi-level changes, which improves the ability to identify damage on different scales. Extensive experiments on the large-scale building damage assessment dataset xBD demonstrate that our approach provides new state-of-the-art results. Source code is publicly available at https://github.com/mj129/D2ANet. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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