Search

• The objective of the work is to develop CNN-based DL model for LULC classification. • An assessment of LULC is carried out for the classified maps of 2011 and 2021. • The change detection analysis of Chamarajanagar district is performed for one decade. • The proposed method is outperformed, with an accuracy of 95.27 % and 94.57 %. The change detection analysis of land use land cover (LULC) is an important task in several fields and applications such as environmental monitoring, urban planning, disaster management, and climate change studies. This study focuses on the use of remote sensing (RS) and geographic information systems (GIS) to identify the changes in Chamarajanagar district, which is located in Karnataka state, South India. This paper mainly focuses on the classification and change detection analysis of LULC in 2011 and 2021 using linear imaging self-scanning sensor-III (LISS-III) satellite images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning classification method for LULC classification. The main objective of the research work is to perform an accurate change detection of the Chamarajanagar district using the classified maps of the years 2011 and 2021. The proposed classification method is outperformed, with a classification accuracy of 95.27 % and 94.57 % for LISS-III satellite imagery of the years 2011 and 2021 respectively. Further, change detection analysis has been carried out using classified maps and results show a decline of 3.23 sq. km, 22.7 sq. km, and 3.83 sq. km in the areas covered by vegetation, agricultural land, and forest area, respectively. In other classes, such as built-up, water bodies, and barren land, an increase in land cover was observed by 5.59 sq. km, 1.99 sq. km, and 20.92 sq. km, respectively. [ABSTRACT FROM AUTHOR]

The failure to notice changes to objects is called change blindness, and it is often studied with the flicker task. Observers performing this task see two rapidly alternating but slightly different stimulus displays that are usually photos of real-world scenes. In order to detect the change, they must compare objects in the pre-change scene with objects at the same locations in the post-change scene to determine whether they are the same or different. It has been proposed that change blindness can occur when the memory representation of a pre-change object is incomplete and thereby impairs the same/different comparison with the post-change object at the same location. It has also been proposed that even with intact pre-change object memory representations, failure of same/different comparisons for other reasons can cause change blindness. The goal of the current study was to conduct flicker task experiments to examine both proposals. We conducted the current experiments with non-photographic stimuli, varied the degree of feature-based change of colored lines and found that the greater degree of change, the faster the same/different comparisons, and the faster that changes were detected. We also examined the representation integrity account of change blindness by comparing detection times of target objects that underwent a single feature change with those that underwent multiple sequential feature changes. The latter were detected faster, which suggests that multiple identities of these sequentially changing objects were stored in memory and facilitated change detection. In another experiment we found that objects that underwent multiple sequential feature changes were not detected as fast as those that underwent multiple simultaneous feature changes. This is consistent with the representation account of change blindness and suggests that memories of multiple sequentially changing object identities are transient and may become less complete over time. And more generally that multiple simultaneous and multiple sequential feature-based changes to these stimuli can show the extent to which memory is involved when searching for flicker task targets. The results of the current study indicate that both the comparison failure and the representation integrity proposals can account for change blindness. [ABSTRACT FROM AUTHOR]

Adaptive forest management strategies require accurate detection of forest disturbance, in various spatial scales. Synthetic Aperture Radar (SAR) data can provide information about the forest attributes, penetrating the canopy at different levels under any weather and lighting conditions. ICEYE consists of the largest constellation of SAR satellites, enabling very high spatial and temporal resolution. In this study, ICEYE data were investigated in the detection of storm damage, in a fir forest with complex topography which was recently hit by the Daniel storm, resulting in severe damage to the forest structure (FS) and alluvium depositions (AD). To identify the best potential for storm damage detection using ICEYE data, an unsupervised change detection approach was employed combining wavelet transform and adaptive thresholds at spatial scales of 0.5 m (R05), 1 m (R1), 2 m (R2) and 3 m (R3). Additionally, two morphological filters were applied in best-performing resolutions to assess the impact of post-processing on the detection accuracy. Finally, FS and AD damage were investigated separately in order to provide detailed information about the detection capabilities of ICEYE. The results showcased that R1 (UA = 32.35, PA = 18.34 and K = 0.31) provided the best detection performance, followed by R05 (UA = 20.62, PA = 20.12 and K = 0.19). Furthermore, the employment of morphological filters slightly increased UA and Kappa metrics in both R1(UA = 33.40 and K = 0.32) and R05 (UA = 25.60 and K = 0.24), suggesting that post-processing is necessary to mitigate false detections. Regarding the investigation of AD and FS damage, it was revealed that post-processed ICEYE data in both R05 and R1 are capable of identifying AD with satisfying accuracy (UA = 47.41, PA = 22.36, K = 0.47), while FS damage detection is more challenging (UA = 10.15%-14.40%, PA = 14.55%-15.11%, and Kappa = 0.10-0.17). Overall, this study demonstrated that ICEYE can be used to detect storm-affected areas in mountainous forest ecosystems, especially in cases where detection with other methods is not feasible. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) change detection (CD) has recently achieved remarkable success thanks to convolutional neural networks (CNNs). However, due to the limited receptive fields of CNN models, existing methods are prone to generating pseudo detections or missed detections. In this letter, we propose a difference enhanced-Swin Transformer network (DEST) for accurate and robust change detection in RS images. First, we design a difference enhancement module (DEM) in the feature extraction stage to boost the feature learning of differences for dual-temporal images at each level. Second, to enlarge the receptive fields of networks and capture more changed details, we apply a Swin Transformer module to the difference features to model the global contextual information. Third, to avoid the semantic loss and simultaneously solve the problem of uneven contributions of features at different levels, we design a feature weight fusion module (FWFM) to effectively aggregate multi-level feature difference maps. Extensive experimental results on two publicly available benchmarks demonstrate that the proposed method is superior to some state-of-the-art change detection models. [ABSTRACT FROM AUTHOR]

Small object change detection (SOCD) based on high-spatial resolution (HSR) images is of significant practical value in applications such as the investigation of illegal urban construction, but little research is currently available. This study proposes an SOCD model called TMACNet based on a multitask network architecture. The model modifies the YOLOv8 network into a Siamese network and adds structures, including a feature difference branch (FDB), temporal mutual attention layer (TMAL) and contextual attention module (CAM), to merge differential and contextual features from different phases for the accurate extraction and analysis of small objects and their changes. To verify the proposed method, an SOCD dataset called YZDS is created based on unmanned aerial vehicle (UAV) images of small-scale solar water heaters on rooftops. The experimental results show that TMACNet exhibits strong resistance to image registration errors and building height displacement and prevents error propagation from object detection to change detection originating from overlay-based change detection. TMACNet also provides an enhanced approach to small object detection from the perspective of multitemporal information fusion. In the change detection task, TMACNet exhibits notable F1 improvements exceeding 5.96% in comparison with alternative change detection methods. In the object detection task, TMACNet outperforms the single-temporal object detection models, increasing accuracy with an approximately 1–3% improvement in the AP metric while simplifying the technical process. [ABSTRACT FROM AUTHOR]

Buildings are primary locations for human activities and key focuses in the military domain. Rapidly detecting damaged/changed buildings (DCB) and conducting detailed assessments can effectively aid urbanization monitoring, disaster response, and humanitarian assistance. Currently, the tasks of object detection (OD) and change detection (CD) for DCB are almost independent of each other, making it difficult to simultaneously determine the location and details of changes. Based on this, we have designed a cross-task network called SDCINet, which integrates OD and CD, and have created four dual-task datasets focused on disasters and urbanization. SDCINet is a novel deep learning dual-task framework composed of a consistency encoder, differentiation decoder, and cross-task global attention collaboration module (CGAC). It is capable of modeling differential feature relationships based on bi-temporal images, performing end-to-end pixel-level prediction, and object bounding box regression. The bi-direction traction function of CGAC is used to deeply couple OD and CD tasks. Additionally, we collected bi-temporal images from 10 locations worldwide that experienced earthquakes, explosions, wars, and conflicts to construct two datasets specifically for damaged building OD and CD. We also constructed two datasets for changed building OD and CD based on two publicly available CD datasets. These four datasets can serve as data benchmarks for dual-task research on DCB. Using these datasets, we conducted extensive performance evaluations of 18 state-of-the-art models from the perspectives of OD, CD, and instance segmentation. Benchmark experimental results demonstrated the superior performance of SDCINet. Ablation experiments and evaluative analyses confirmed the effectiveness and unique value of CGAC. [ABSTRACT FROM AUTHOR]

Change detection, as a crucial task in the field of Earth observation, aims to identify changed pixels between multi-temporal remote-sensing images captured at the same geographical area. However, in practical applications, there are challenges of pseudo changes arising from diverse imaging conditions and different remote-sensing platforms. Existing methods either overlook the different imaging styles between bi-temporal images, or transfer the bi-temporal styles via domain adaptation that may lose ground details. To address these problems, we introduce the disentangled representation learning that mitigates differences of imaging styles while preserving content details to develop a change detection framework, named Content Cleansing Network (CCNet). Specifically, CCNet embeds each input image into two distinct subspaces: a shared content space and a private style space. The separation of style space aims to mitigate the discrepant style due to different imaging condition, while the extracted content space reflects semantic features that is essential for change detection. Then, a multi-resolution parallel structure constructs the content space encoder, facilitating robust feature extraction of semantic information and spatial details. The cleansed content features enable accurate detection of changes in the land surface. Additionally, a lightweight decoder for image restoration enhances the independence and interpretability of the disentangled spaces. To verify the proposed method, CCNet is applied to five public datasets and a multi-temporal dataset collected in this study. Comparative experiments against eleven advanced methods demonstrate the effectiveness and superiority of CCNet. The experimental results show that our method robustly addresses the issues related to both temporal and platform variations, making it a promising method for change detection in complex conditions and supporting downstream applications. [ABSTRACT FROM AUTHOR]

Detecting the influence of temperature on urban vegetation is useful for planning urban biodiversity conservation efforts, since temperature affects several ecosystem processes. In this study, the relationships between land surface temperature (LST) and vegetation phenology events (start of growing season, SOS; end of growing season, EOS; peak phenology) was examined in native savannah woodland and grass parcels of a hot climate town. For comparison, similar woodland and grass parcels on the town's periphery, and a wetland, were used. The vegetation parcel LST values (°C) in one calendar year (2023) were obtained from Landsat-8 (L8) and Landsat-9 (L9) thermal imagery, whose combination yielded an 8-day image frequency. Phenology changes relative to seasonal air temperature and LST were determined using vegetation index (VI) values computed from accompanying 30 m resolution L8-L9 non-thermal bands: the Normalised Difference Vegetation Index (NDVI) and one improved VI, the Soil Adjusted Vegetation Index (SAVI). Higher imaging frequency, 250 m resolution NDVI and Enhanced Vegetation Index (EVI) MOD13Q1 layers supplemented the L8-L9 VIs. LST correlated highly with air temperature (p < 0.001). On nearly all L8-L9 image dates, the urban vegetation parcel's mean LST was higher (p < 0.001) than that at its peri-urban equivalent. Improved VIs (SAVI, EVI) detected some phenology events to have occurred slightly earlier than detected by the NDVI. Associated with the higher LST, the SOS was earlier in the urban than in the peri-urban woodland. This association has scarcely been demonstrated in savannah vegetation, necessitating proactive efforts to reduce potential biodiversity effects. [ABSTRACT FROM AUTHOR]

In large open-pit coal mine projects, internal dump slope failure is a serious issue for industry. Slope failure affects haul roads, mine machinery, and miners' lives and hampers coal production operations. Dump slope failure events will not occur suddenly, and progressive displacement will occur in the slope before failure. Therefore, dump slope monitoring is essential for the safety of humans and machinery. Generally, the mine slope displacement is measured using a conventional survey approach. However, conventional surveys cannot measure minor changes in a large dump area. Therefore, massive dump displacement monitoring is a tedious task for surveyors. This study used the unmanned aerial vehicle (UAV) close-range photogrammetry survey to introduce an intelligent and reliable approach for dump change detection and displacement monitoring. A UAV close-range photogrammetry survey was conducted to collect RGB images, and dump surface point cloud data (PCD) were generated using the structure-from-motion (SfM) computing algorithm. The ground control points (GCPs) were also measured using a coordinate measurement survey for geo-reference points. For dump surface PCD and 3D reconstruction, the Pix-4D Mapper image processing tool was used. PCDs were generated for two months and used for dump slope surface change and displacement monitoring using the cloud-to-cloud (C2C) computing algorithm. The PCD was compared with the reference PCD, and the results of this investigation indicated minor and major changes on the dump slope and top surfaces. This approach is inexpensive and reliable for large dump slope change detection and displacement monitoring. [ABSTRACT FROM AUTHOR]

Mangrove forests, being high-yielding ecosystems, often dominate the intertidal sites along equatorial and subtropical coasts. Despite the known significance of mangroves to the coastal ecology, especially fisheries, deforestation remains a severe danger due to coercion for forest products, ground transformation for aquaculture, and seaside urban growth. Remote sensing is integral in mapping and analysing changes in mangrove forests' areal extent and spatial patterns due to natural disasters and anthropogenic causes over the last three decades. This work depicts remote sensing analysis for change detection in mangrove forest land use land cover from 2014 to 2019. Indian Remote-Sensing Satellite Resourcesat-2 LISS-IV datasets have been used for analysis. A comparison with the Sentinel-2A dataset and two machine learning models: Random Forest and Classification and Regression Tree, has been performed with 2019 data. This work identifies CART as a suitable choice for supervised landform classification utilising remotely sensed geophysical data that is used to decipher spatial changes concurred over time. An overall growth in the mangrove cover was observed from 2014 to 2019, from 86.26 to 89.63 km 2 , along the Mumbai coastline. Spatial comparison over the years shows the growth and loss of land-use cover areas. The performance metrics such as overall accuracy, producer accuracy, Kappa coefficient, and Matthews correlation coefficient are computed. The experiments were conducted using the Google Earth Engine, a powerful cloud computing platform. [ABSTRACT FROM AUTHOR]

Fault classification is crucial in fault mitigation to maintain selectivity in tripping only the faulted phase or zone in power system networks. However, inverter-interfaced renewable energy sources' unique fault current profile poses challenges to classifiers designed for conventional systems, which are inadequate in the presence of renewable energy resources such as inverter-interfaced photovoltaic (PV) or wind turbine systems in the grid. The inverters have internal protection schemes that trip during unbalanced conditions; however, in grids with high penetration of renewable energy, the inverter must ride through the fault and let relays protect the system. Moreover, the different control strategies for inverters can make the fault current small enough to be unreliable to use as a parameter in fault classifications. This study proposes a reliable fault classification method that can accurately identify faults in power systems with high penetration of renewable energy sources. This paper discusses a machine learning (ML)-based classifier using phase current and voltage magnitude to classify faults. The performance of the proposed classifier is validated against different fault scenarios in power systems like the IEEE 9-bus system. The classifier discussed in this paper achieved a satisfactory accuracy of 99.78% with voltage measurements for test conditions within three-quarters of a cycle. The classifier can be used for any three-phase system to provide correct faulted phase information to other protection components. The same methodology is extended to identify evolving faults, achieving an accuracy of 99.6% in determining the evolving fault type. Thus, the proposed ML-based classifier provides a reliable and accurate method for fault classification in power systems with high penetration of renewable energy sources. [ABSTRACT FROM AUTHOR]

The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods. [ABSTRACT FROM AUTHOR]

The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1's Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution. HIGHLIGHTS: Dense vegetation reduces the ability to retrieve surface soil moisture at a kilometre scale from Sentinel-1 backscatter which is currently available on the Copernicus Global Land Service portal. Applying selective masking for vegetation during the resampling phase improves Sentinel-1 sensitivity to soil moisture. A novel vegetation-corrected Sentinel-1 surface soil moisture product is processed over Europe for the period 2016–2022 included. The Sentinel-1 forest mask improves the Sentinel-1 SSM product correlation and seasonality compared to both modelled and in-situ datasets. [ABSTRACT FROM AUTHOR]

Permanent grasslands play a very important role in the landscape. The loss of permanent grasslands and their subsequent conversion into arable land create erosion-prone agricultural areas in the landscape and have a negative impact on the biodiversity. From this point of view, there is a need for the accurate and effective monitoring of changes in the agricultural landscape along with an assessment of the influence of the agricultural policies on the landscape. Sentinel-2 from the Copernicus programme has improved options for the implementation of remote sensing data into the monitoring of agricultural land. The aim of this study was to evaluate the potential of H2O library and within implemented Automachine learning function (AutoML) and its stacked ensembles for mapping changes from grasslands to arable lands. All results show high overall accuracy from 93.5% to 96.6% and high values of area under the ROC curve (0.94–0.98). Stacked ensembles appear to be the most accurate machine learning models for mapping changes from grasslands to arable lands. The importance of several biological predictors has been tested (FAPAR, FCOVER, LAI, NDVI, etc.) with the help of a heatmap that is part of AutoML function of H2O library. [ABSTRACT FROM AUTHOR]

Landslide mapping inventories are crucial for disaster prevention and risk mitigation. Remote sensing uses remote sensors that record data from the Earth's surface encoded in digital images distributed in electromagnetic spectrum ranges, allowing us access to various types of information. This, in conjunction with appropriate spatial analysis and modeling techniques, allows us to monitor the phenomena, such as landslides, that put man-nature coupled systems at risk. This paper presents a practical alternative for integrating landslide inventories in the central area of the state of Guerrero in Mexico by using the maximum entropy model (MaxEnt), a machine learning algorithm oriented to the potential prediction of patterns using continuous change (CC) maps as input. These maps were obtained using the unsupervised change detection methods linear regression and difference applied to transformed images, the normalized difference vegetation index (NDVI), and principal component analysis (PCA). The selection of supplementary input data was made by using the jackknife test to assess the contribution of the main determinant factors of slope stability: lithology (L), angular slopes (AS), and terrain orientation (TO). Ground truth landslide samples were used for the algorithm training (2/3) and the accuracy assessment of the final inventory map (1/3). The landslide inventory map derived by combining the MaxEnt model, the thresholding by the secant method, and the discrimination of pixels with slope values less than 5° reveals a high accuracy and visual concordance with reality, reaching 3.0% and 3.5% in commission and omission errors, a Kappa concordance index of 93.37%, and an AUC of 0.75, indicating MaxEnt is a practical and efficient tool that allows for the rapid and accurate generation of reliable maps for the detection of landslides. [ABSTRACT FROM AUTHOR]

The use of bi-temporal remote sensing images for detecting changes in land cover is an important means of obtaining surface change information, thus contributing to urban governance and ecological environment monitoring. In this article, we propose a deep learning model named Siam-UNet3+ for high-resolution remote sensing image change detection. This model integrates the full-scale skip connections and full-scale deep supervision of the network UNet3+, which can achieve the multi-scale feature fusion of remote sensing images, effectively avoiding the locality disadvantage of convolution operations. Different from UNet3+, Siam-UNet3+ has made major improvements, including the following: (1) incorporating a Siamese network in the encoder, which can process bi-temporal remote sensing images in parallel; (2) leveraging the residual module as the backbone, which can avoid gradient vanishing (or exploding) and model degradation problems; (3) adding a Triplet Attention module to the decoder, which can avoid information redundancy that may occur in full-scale skip connections and increase the ability to focus on changing patterns; and (4) designing a hybrid loss function consisting of focal loss and dice loss, which is more suitable for remote sensing image change detection tasks. In this study, we conducted change detection experiments using the publicly available LEVIR-CD dataset, as well as two local datasets in Beijing. Through comparative experiments with five other models and ablation experiments, the proposed model Siam-UNet3+ in this article demonstrated significant advantages and improvements in four evaluation metrics, namely, precision, recall, F1-score, and overall accuracy (OA), proving to have great potential in the application to highresolution remote sensing image change detection tasks. [ABSTRACT FROM AUTHOR]

• Amplitude and synchrony of 40-Hz ASSR was decreased by sound pressure change. • Decrease of amplitude and synchrony depended on magnitude of sound pressure change. • Transient 40-Hz ASSR desynchronization might be an index of change detection. A periodic sound with a fixed inter-stimulus interval elicits an auditory steady-state response (ASSR). An abrupt change in a continuous sound is known to affect the brain's ongoing neural oscillatory activity, but the underlying mechanism has not been fully clarified. We investigated whether and how an abrupt change in sound intensity affects the ASSR. The control stimulus was a train of 1-ms clicks with a sound pressure level (SPL) of 70 dB at 40 Hz for 1000 ms. In addition to the control stimulus, we applied six stimuli with changes consisting of a 500-ms train at 70 dB followed by a 500-ms similar train with louder clicks of 75, 80, or 85 dB or weaker clicks of 55, 60, or 65 dB. We obtained the magnetoencephalographic responses from 15 healthy subjects while presenting the seven stimuli randomly. The two-dipole model obtained for the 40-Hz ASSR in the control condition was applied to all of the stimulus conditions for each subject, and then the time–frequency analysis was conducted. We observed that both the amplitude and the inter-trial phase coherence of the 40-Hz ASSR transiently decreased and returned to the steady state after the change onset, i.e., the desynchronization of 40-Hz ASSR. The degree of desynchronization depended on the magnitude of the change regardless of whether the sound intensity increased or decreased, which might be a novel neurophysiological index of cerebral response driven by a change in the sensory environment. [ABSTRACT FROM AUTHOR]

Urbanization and changes in land use are crucial for national development. Managing and monitoring these changes have become easier and more accurate due to advancements in imaging systems and change detection methods. In recent years, convolutional neural networks have gained significant attention and advancement in remote sensing change detection research. However, designing a model that reduces computational complexity while improving detection accuracy is still under discussion. In this regard, an efficient model based on pseudo-siamese convolutional networks had been developed in this research, which by combining multi-scale features at different levels, increased the accuracy of the final change map, especially in images with both different spatial resolutions and scales. Our model is inspired by Scale Invariant Feature Transform (SIFT), which uses multiple scale-spaces to identify scale-independent keypoints in images. We designed our model based on this concept, in which After passing the bi-temporal images through the encoder network and making the initial feature maps, the feature learning process takes place at several levels of different scales and finally, the change map is calculated. In addition, the proposed architecture leverages point-wise, depth-wise, and neighboring features across various scale levels to harness the optimal features present in high-resolution remote sensing images. Simplicity in design, effectiveness in calculations, and accuracy make our model surpass popular methods such as BIT, DSIFN, etc. The evaluation results on 4 different datasets and comparison with 12 state-of-the-art models demonstrate that the proposed method outperforms other popular models in change detection. Available at: https://github.com/farhadinima75/OctaveNet. [ABSTRACT FROM AUTHOR]

Flood disasters are frequent, sudden, and have significant chain effects, seriously damaging infrastructure. Remote sensing images provide a means for timely flood emergency monitoring. When floods occur, emergency management agencies need to respond quickly and assess the damage. However, manual evaluation takes a significant amount of time; in current, commercial applications, the post-disaster flood vector range is used to directly overlay land cover data. On the one hand, land cover data are not updated in time, resulting in the misjudgment of disaster losses; on the other hand, since buildings block floods, the above methods cannot detect flooded buildings. Automated change-detection methods can effectively alleviate the above problems. However, the ability of change-detection structures and deep learning models for flooding to characterize flooded buildings and roads is unclear. This study specifically evaluated the performance of different change-detection structures and different deep learning models for the change detection of flooded buildings and roads in very-high-resolution remote sensing images. At the same time, a plug-and-play, multi-attention-constrained, deeply supervised high-dimensional and low-dimensional multi-scale feature fusion (MSFF) module is proposed. The MSFF module was extended to different deep learning models. Experimental results showed that the embedded MSFF performs better than the baseline model, demonstrating that MSFF can be used as a general multi-scale feature fusion component. After FloodedCDNet introduced MSFF, the detection accuracy of flooded buildings and roads changed after the data augmentation reached a maximum of 69.1% MIoU. This demonstrates its effectiveness and robustness in identifying change regions and categories from very-high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved. [ABSTRACT FROM AUTHOR]

To address the challenges of accurately segmenting irregular building boundaries in complex urban environments faced by existing remote sensing change detection methods, this paper proposes a building change detection network based on multilevel geometric representation optimization using frame fields called BuildingCDNet. The proposed method employs a multi-scale feature aggregation encoder–decoder architecture, leveraging contextual information to capture the characteristics of buildings of varying sizes in the imagery. Cross-attention mechanisms are incorporated to enhance the feature correlations between the change pairs. Additionally, the frame field is introduced into the network to model the complex geometric structure of the building target. By learning the local orientation information of the building structure, the frame field can effectively capture the geometric features of complex building features. During the training process, a multi-task learning strategy is used to align the predicted frame field with the real building outline, while learning the overall segmentation, edge outline, and corner point features of the building. This improves the accuracy of the building polygon representation. Furthermore, a discriminative loss function is constructed through multi-task learning to optimize the polygonal structured information of the building targets. The proposed method achieves state-of-the-art results on two commonly used datasets. [ABSTRACT FROM AUTHOR]

Remote sensing change detection (RSCD) aims to utilize paired temporal remote sensing images to detect surface changes in the same area. Traditional CNN-based methods are limited by the size of the receptive field, making it difficult to capture the global features of remote sensing images. In contrast, Transformer-based methods address this issue with their powerful modeling capabilities. However, applying the Transformer architecture to image processing introduces a quadratic complexity problem, significantly increasing computational costs. Recently, the Mamba architecture based on state-space models has gained widespread application in the field of RSCD due to its excellent global feature extraction capabilities and linear complexity characteristics. Nevertheless, existing Mamba-based methods lack optimization for complex change areas, making it easy to lose shallow features or local features, which leads to poor performance on challenging detection cases and high-difficulty datasets. In this paper, we propose a Mamba-based RSCD network for difficult cases (DC-Mamba), which effectively improves the model's detection capability in complex change areas. Specifically, we introduce the edge-feature enhancement (EFE) block and the dual-flow state-space (DFSS) block, which enhance the details of change edges and local features while maintaining the model's global feature extraction capability. We propose a dynamic loss function to address the issue of sample imbalance, giving more attention to difficult samples during training. Extensive experiments on three change detection datasets demonstrate that our proposed DC-Mamba outperforms existing state-of-the-art methods overall and exhibits significant performance improvements in detecting difficult cases. [ABSTRACT FROM AUTHOR]

Semi-supervised learning has gained significant attention in the field of remote sensing due to its ability to effectively leverage both a limited number of labeled samples and a large quantity of unlabeled data. An effective semi-supervised learning approach utilizes unlabeled samples to enforce prediction consistency under minor perturbations, thus reducing the model's sensitivity to noise and suppressing false positives in change-detection tasks. This principle underlies consistency regularization-based methods. However, while these methods enhance noise robustness, they also risk overlooking subtle but meaningful changes, leading to information loss and missed detections. To address this issue, we introduce a simple yet efficient method called Sample Inflation Interpolation (SII). This method leverages labeled sample pairs to mitigate the information loss caused by consistency regularization. Specifically, we propose a novel data augmentation strategy that generates additional change samples by combining existing supervised change samples with calculated proportions of change areas. This approach increases both the quantity and diversity of change samples in the training set, effectively compensating for potential information loss and reducing missed detections. Furthermore, to prevent overfitting, small perturbations are applied to the generated sample pairs and their labels. Experiments conducted on two public change detection (CD) datasets validate the effectiveness of our proposed method. Remarkably, even with only 5% of labeled training data, our method achieves performance levels that closely approach those of fully supervised learning models. [ABSTRACT FROM AUTHOR]

Remote sensing image change detection is one of the research focuses in the field of remote sensing. At present, most of them are deep learning methods, which mainly use single channel or siamese network to extract features, which can effectively extract change features. However, with the increasing resolution of remote sensing images, the single feature extraction method is susceptible to the influence of irrelevant details, which leads to the insufficient segmentation ability of the changed and unchanged regions in the detection results. Therefore, this paper proposed a fire-new triple-channel regionenhancement change detection network to enhance feature extraction capability from multiple perspectives. Firstly, the method constructed a triple-channel region-enhancement encoder, and used three feature extraction channels to extract similarity information, comprehensiveness information and difference information in a directional manner. At every scales of the encoder, region-separation enhancement modules were able to augment channel 2 with channels 1 and 3, which was beneficial to obtain better effect of changing region segmentation. Secondly, it designed a layer interaction-guidance fusion decoder, and used the interactive guidance between higher-level and lower-level features. So the decoder fused effectively the different-level features by the mutual guidance between high-level features and low-level features. Finally, it used an adaptive weight based on information entropy, which gave more attention to high entropy regions, to optimize loss function. Then, the new loss function improved the training process of the network model. The results of experiment on common datasets show that this network achieves better detection accuracy than the contrast networks. [ABSTRACT FROM AUTHOR]

Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland's multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes. [ABSTRACT FROM AUTHOR]

The existing change detection (CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is essentially dependent on change regions of interest (CRoIs), meaning that the CD results are directly determined by the semantics changes in interest, making its primary image factor semantic of interest rather than visual. The ViFi-CD paradigm can only assign specific semantics of interest to specific change features extracted from visual differences, leading to the inevitable omission of potential CRoIs and the inability to adapt to different CRoI CD tasks. In other words, changes in other CRoIs cannot be detected by the ViFi-CD method without retraining the model or significantly modifying the method. This paper introduces a new CD paradigm, the semantic-first CD (SeFi-CD) paradigm. The core idea of SeFi-CD is to first perceive the dynamic semantics of interest and then visually search for change features related to the semantics. Based on the SeFi-CD paradigm, we designed Anything You Want Change Detection (AUWCD). Experiments on public datasets demonstrate that the AUWCD outperforms the current state-of-the-art CD methods, achieving an average F1 score 5.01% higher than that of these advanced supervised baselines on the SECOND dataset, with a maximum increase of 13.17%. The proposed SeFi-CD offers a novel CD perspective and approach. [ABSTRACT FROM AUTHOR]

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs. [ABSTRACT FROM AUTHOR]

Remote sensing image change detection has significant applications across various fields. In recent years, the powerful feature extraction capability of deep learning technology has introduced innovative approaches to change detection methodologies. However, the accuracy of convolutional neural network algorithms hinges heavily on labelled data, necessitating substantial manpower and resources to generate labelled samples. Therefore, this paper proposes an unsupervised change detection model called CIUCD. This model operates on the fundamental premise of unsupervised change detection, employing a generator and a segmenter for iterative training. During each iteration, the generator and segmenter update and optimize their parameters, with the segmenter leveraging the generated change images as labels for training purposes. This iterative labelling process circumvents the need for manually labelled data, thereby mitigating the difficulty of obtaining change detection labels for actual remote sensing images. To validate the efficacy of the proposed model, we conducted experiments using actual remotely sensed images from the 2023 Turkey earthquake and the 2022 Afghanistan earthquake events. The results show that our method enhances the F1, Pr, Re and OA by 5.98%, 2.05%, 11.82% and 8.50% (Turkey earthquake), and by 9.54%, 5.87%, 4.52% and 11.34% (Afghanistan earthquake), respectively, when compared to the unsupervised change detection algorithm KPCA-Mnet. Experiments on real remote sensing images of 1000 × 1000 pixels showed that the CIUCD algorithm not only accurately detect larger change areas but is also capable of identifying changes in subtle objects such as solar panels. Additionally, the CIUCD algorithm is resilient to natural environmental factors like the angle of illumination, enabling precise predictions of changes in the actual remote sensing images and yielding clearer detection results. The performance of the CIUCD algorithm remains outstanding even when applied to larger images with a resolution of 3000 × 3000 pixels. [ABSTRACT FROM AUTHOR]

The regional ecological environment has become a significant subject of study due to the dynamics of the ecosystem, which is represented by vegetation, under the influence of human activities. The objective of this research is to demonstrate the implementation and effectiveness of the forest canopy density (FCD) model in generating a map that illustrates changes in forest canopy density using multitemporal remote sensing data in Tabunio watershed. The methodology relies on vegetation index, including the normalized difference vegetation index (NDVI), shadow index (SI), and bare soil index (BI), to generate a composite vegetation index (CVI). FCD uses multitemporal remote sensing data from Landsat TM images from 2005 to 2020, which have been utilized to accomplish multisource categorization. The findings indicated that the vegetation coverage of the Tabunio watershed presented a predominant pattern of high coverage in the northeastern and eastern regions, whereas most areas of the western region had low coverage; (2) vegetation cover from 2005 to 2020 is dominated by sparse to very dense vegetation cover classes; (3) changes in vegetation cover over two decades are very significant. The expansion of plantation land in 2005 caused a lot of non-vegetated land, which gradually changed in the following year period along with plant growth. At the end of 2020, the percentage of very dense vegetation became increasingly dominant, which was around 42 percent. The results of the study indicate the three biophysical index (NDVI, SI, and BI) used in this model approach were appropriate for precisely discriminating across all canopy density classes, as seen by the overall producer's accuracy of 81.3%. FCD model in multitemporal data can helps in the early identification of deforestation or forest degradation activities. Furthermore, the FCD model may have certain constraints, as it requires an understanding of ground conditions to establish threshold values for each class. [ABSTRACT FROM AUTHOR]

Alberta Transportation and Economic Corridors is currently working towards the development of a formalized geotechnical asset management program, which requires linking rockfall geohazard condition assessment tools with rock slope performance. Integrating the use of remote sensing technologies with condition assessment tools based on engineering geology concepts may provide transportation agencies with a methodological basis to aid in the prioritization of capital expenditure for rockfall geohazard sites. Presented in this paper is a methodology to develop a direct correlation between slope condition assessments and slope performance metrics derived from change detection. The methodology is demonstrated using an initial database of change detection results for three rockfall geohazard sites in Alberta, Canada where a suite of rock slope and rock mass rating systems were applied, including Alberta's current condition assessment tool, the Geohazards Risk Management Program Risk Level rating. Rockfall metrics including annual failure volumes and annual frequencies for events greater than or equal to 1 m3 were derived from the change detection results and compared with the results of the rock slope and rock mass rating tools for each of the study sites. Strong correlations were achieved between each rating system and the rockfall metrics derived from the change detection results. This methodology provides a direct correlation between practical condition assessment tools and rock slope performance monitoring techniques to be used along transportation corridors to improve prioritization of maintenance and remediation based on a quantified level of hazard. [ABSTRACT FROM AUTHOR]

Efficient monitoring of crop water requirements is crucial for assessing the impacts of major irrigation projects, such as the Kaleshwaram lift irrigation project, both before and after their implementation. These projects can significantly change agricultural practices and water usage patterns, necessitating thorough evaluations to ensure sustainable water management and agricultural resilience. The main aim of this study is to evaluate and compare crop water needs during the winter (rabi) seasons of 2018-2019 and 2022-2023 across the command area of the project. This is achieved by mapping major crops and their respective length of growing periods across the study area using sentinel-2 satellite data and ground data, and quantifying crop water requirements using reference evapotranspiration and FAO crop coefficients. Results reveal a significant shift towards rice cultivation, with an over 80% increase in the winter season of 2022-2023 compared to 2018-2019, indicating substantial escalations in crop water requirements. These findings provide valuable insights into agricultural transformations induced by largescale irrigation interventions, emphasizing the need for sustainable water management practices to ensure agricultural resilience and resource conservation in similar contexts. [ABSTRACT FROM AUTHOR]

With the development and popularization of unmanned aerial vehicle (UAV) equipment, UAV-based remote sensing technology has developed rapidly. With UAV aerial photography platforms, the obtained low altitude remote sensing images of surfaces have high temporal and spatial resolution and can be used for monitoring and change analyses of surface geographic information. As it is impractical to carry complex and large load equipment on a UAV, it is desirable to develop embedded systems for relevant real-time processing with UAV remote sensing technology. This study combines the advantages of UAV remote sensing image processing and embedded development technology to develop a near real-time UAV remote sensing image change detection system, which realizes the fast, accurate, and automatic detection of variations in the target area. Finally, the feasibility, correctness, and practicability of the prototype system are demonstrated with experiments. [ABSTRACT FROM AUTHOR]

Remote sensing images provide a valuable way to observe the Earth's surface and identify objects from a satellite or airborne perspective. Researchers can gain a more comprehensive understanding of the Earth's surface by using a variety of heterogeneous data sources, including multispectral, hyperspectral, radar, and multitemporal imagery. This abundance of different information over a specified area offers an opportunity to significantly improve change detection tasks by merging or fusing these sources. This review explores the application of deep learning for change detection in remote sensing imagery, encompassing both homogeneous and heterogeneous scenes. It delves into publicly available datasets specifically designed for this task, analyzes selected deep learning models employed for change detection, and explores current challenges and trends in the field, concluding with a look towards potential future developments. [ABSTRACT FROM AUTHOR]

Remote sensing image change detection is an important research in the field of remote sensing, which aims to use remote sensing technology and image processing methods to identify the patterns and trends of surface cover changes. In order to gain a deeper understanding of the current development of this area and the technical methods used, a large amount of information and literatures are summarized and analyzed to provide a more comprehensive review of remote sensing image change detection methods. Firstly, the concept and processing flow of change detection are introduced. Then the classification system of change detection methods is summarized from six angles, followed by a review of their development history. Subsequently, the principles and characteristics of various types of change detection methods are outlined, their advantages and disadvantages are briefly analyzed, and the real-world application value of change detection on remotely sensed images is discussed from six aspects. Some problems and shortcomings in the area are briefly analyzed, and some possible ways to improve these problems are proposed, while the obstacles that may be encountered in the practical application of these methods are also predicted. Finally, the change detection methods are summarized, and the future development direction is prospected, in order to better understand the research status and development trend of remote sensing image change detection methods, and provide reference for further research. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome. [ABSTRACT FROM AUTHOR]

This study critically examines the spatial dynamics of urban sprawl and land use changes in Bhilwara, Rajasthan, over the past three decades, utilizing advanced remote sensing and GIS methodologies. The primary objective is to elucidate the patterns of urban expansion and its consequential impacts on green infrastructure. The findings reveal a pronounced increase in built-up areas, with the most significant growth observed towards the north-northwest (NNW) and west-southwest (WSW) directions, driven by the presence of major transportation corridors and proximate urban centers. The LULC classification achieved high accuracy, with Producer's accuracy ranging from 81 to 94.4%, User's accuracy from 73.7 to 100%, and Overall accuracy between 84.5 and 93.8%, accompanied by a Kappa coefficient of 0.78 to 0.83. These results underscore the robust methodology employed and highlight the critical need for integrated urban planning approaches that prioritize sustainable development and green infrastructure preservation. The study's conclusions offer substantial contributions to the academic discourse on urbanization, providing actionable insights for policymakers and urban planners to manage urban growth effectively and sustainably. [ABSTRACT FROM AUTHOR]

The extraction of minerals on an extensive scale, though a catalyst for economic advancement, precipitates notable ecological concerns. In recent years, due to decarbonization initiatives and the closure of numerous open-pit mines, increasing attention and research focus have been directed toward evaluating the effectiveness of ecological restoration in mining areas. This study leverages Landsat series imagery and employs the pseudo-invariant feature (PIF) method for radiometric normalization of remote sensing images, all within the framework of ecological engineering. In light of the significant consideration given to soil erosion and air pollution factors in the acceptance standards for ecological engineering, the Mine Remote Sensing Ecological Index (MRSEI) is developed based on the Pressure-State-Response (PSR) framework. This index is employed to perform spatiotemporal analysis and dynamic monitoring of the ecological quality in the restoration area of Wangping coal mine. The results illustrate that: Compared to the Remote Sensing Ecological Index (RSEI), the first principal component of the MRSEI consolidates the information of various sub-indicators more effectively. This allows for a more objective representation of the ecological quality. From 1990 to 2021, the average value of the MRSEI in the Wangping coal mining area shows an overall upward trend, increasing from 0.429 in 1990 to 0.731 in 2021, representing an improvement of 70.40%. The validation of the MRSEI indicates that this index accurately reflects the objective patterns of local ecological quality changes. Moreover, it is strongly correlated with various individual ecological indicators. The application and promotion of the MRSEI offer valuable insights for policymakers in developing plans for mine ecological restoration projects and strategies for regional coordinated development. [ABSTRACT FROM AUTHOR]

This study investigates land use and land cover (LULC) changes in Samastipur District, Bihar, from 2000 to 2020 using Landsat data and GIS techniques. Our analysis reveals a significant increase in agricultural land from 64.24% to 84.80%, amounting to a rise of 59,615 ha. During the same period, natural vegetation decreased sharply from 20.76% to 3.19%, and water bodies diminished from 2.72% to 1.82%. Settlement areas expanded by 38.30%, while barren land was reduced by 44.65%. Accuracy assessments showed substantial agreement, with Kappa values improving from 0.64 in 2000 to 0.88 in 2020, and overall accuracy rising from 71.62% to 90.74%. The Productivity Rating Index (PRI) for Pusa Farm indicates high suitability for major crops, with PRI values of 82.90% for wheat, 106.57% for sugarcane, and 137.14% for paddy. These findings underscore the dynamic changes in land use and the effectiveness of remote sensing for monitoring and managing agricultural resources, providing valuable insights for sustainable land management and policy-making. [ABSTRACT FROM AUTHOR]

To navigate changes within a highly dynamic and complex environment, it is crucial to compare current visual representations of a scene to previously formed representations stored in memory. This process of mental comparison requires integrating information from multiple sources to inform decisions about changes within the environment. In the present article, we combine a novel systems factorial technology change detection task (Blunden et al., 2022) with a set size manipulation. Participants were required to detect 0, 1, or 2 changes of low and high detectability between a memory and probe array of 1–4 spatially separated luminance discs. Analyses using systems factorial technology indicated that the processing architecture was consistent across set sizes but that capacity was always limited and decreased as the number of distractors increased. We developed a novel model of change detection based on the statistical principles of basic sampling theory (Palmer, 1990; Sewell et al., 2014). The sample size model, instantiated parametrically, predicts the architecture and capacity results a priori and quantitatively accounted for several key results observed in the data: (a) increasing set size acted to decrease sensitivity (d′) in proportion to the square root of the number of items in the display; (b) the effect of redundancy benefited performance by a factor of the square root of the number of changes; and (c) the effect of change detectability was separable and independent of the sample size costs and redundancy benefits. [ABSTRACT FROM AUTHOR]

To improve the accuracy of detecting changes in remote sensing time series, an improved algorithm based on the combination of the antileakage least-squares spectral analysis (ALLSSA) algorithm and detecting breakpoints and estimating segments in trends (DBEST) algorithm is proposed and applied. The method uses the ALLSSA algorithm to decompose the time series and identify the trend components in the time series. Then, the trend segmentation mechanism of the DBEST algorithm is used to detect the changes in the trend component. In this paper, the improved algorithm is evaluated using a simulated time series data set, a time series data set with multiple change points, and data set based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing time series. The results demonstrate that the average detection accuracies of the improved algorithm and DBEST algorithm are 98.4% and 85.2%, respectively, for the simulated time series data set. For the time series data set with multiple change points, the average root mean square errors (RMSEs) of the trend data for the improved and DBEST algorithms are 0.0386 and 0.0331, respectively. The mean normalized residual norms (MNRNs) of the improved and DBEST algorithms are 0.0252 and 0.0351, respectively. Finally, the improved algorithm, DBEST algorithm, and breaks for additive season and trend (BFAST) algorithm are applied to MODIS NDVI data, and their performance with remote sensing data is compared. The improved algorithm has higher detection accuracy and a smaller MNRN, indicating that more information is included in the trend and seasonal components. Therefore, the proposed method is useful for analysing trends in remote sensing time series data. [ABSTRACT FROM AUTHOR]

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar tomography (TomoSAR) has gained significant attention for three-dimensional (3D) imaging in urban environments. A notable limitation of traditional TomoSAR approaches is their primary focus on persistent scatterers (PSs), disregarding targets with temporal decorrelated characteristics. Temporal variations in coherence, especially in urban areas due to the dense population of buildings and artificial structures, can lead to a reduction in detectable PSs and suboptimal 3D reconstruction performance. The concept of partially coherent scatterers (PCSs) has been proven effective by capturing the partial temporal coherence of targets across the entire time baseline. In this study, an novel approach based on an iterative sub-network generation method is introduced to leverage PCSs for enhanced 3D reconstruction in dynamic environments. We propose a coherence constraint iterative variance analysis approach to determine the optimal temporal baseline range that accurately reflects the interferometric coherence of PCSs. Utilizing the selected PCSs, a 3D imaging technique that incorporates the iterative generation of sub-networks into the SAR tomography process is developed. By employing the PS reference network as a foundation, we accurately invert PCSs through the iterative generation of local star-shaped networks, ensuring a comprehensive coverage of PCSs in study areas. The effectiveness of this method for the height estimation of PCSs is validated using the TerraSAR-X dataset. Compared with traditional PS-based TomoSAR, the proposed approach demonstrates that PCS-based elevation results complement those from PSs, significantly improving 3D reconstruction in evolving urban settings. [ABSTRACT FROM AUTHOR]

In the field of remote sensing (RS), change detection (CD) methods are critical for analyzing the quality of images shot over various geographical areas, particularly for high-resolution images. However, there are some shortcomings of the widely used Convolutional Neural Networks (CNNs) and Transformers-based CD methods. The former is limited by its insufficient long-range modeling capabilities, while the latter is hampered by its computational complexity. Additionally, the commonly used information-fusion methods for pre- and post-change images often lead to information loss or redundancy, resulting in inaccurate edge detection. To address these issues, we propose an Iterative Mamba Diffusion Change Detection (IMDCD) approach to iteratively integrate various pieces of information and efficiently produce fine-grained CD maps. Specifically, the Swin-Mamba-Encoder (SME) within Mamba-CD (MCD) is employed as a semantic feature extractor, capable of modeling long-range relationships with linear computability. Moreover, we introduce the Variable State Space CD (VSS-CD) module, which extracts abundant CD features by training the matrix parameters within the designed State Space Change Detection (SS-CD). The computed high-dimensional CD feature is integrated into the noise predictor using a novel Global Hybrid Attention Transformer (GHAT) while low-dimensional CD features are utilized to calibrate prior CD results at each iterative step, progressively refining the generated outcomes. IMDCD exhibits a high performance across multiple datasets such as the CDD, WHU, LEVIR, and OSCD, marking a significant advancement in the methodologies within the CD field of RS. The code for this work is available on GitHub. [ABSTRACT FROM AUTHOR]

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]

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]

Remote sensing image change detection is crucial for urban planning, environmental monitoring, and disaster assessment, as it identifies temporal variations of specific targets, such as surface buildings, by analyzing differences between images from different time periods. Current research faces challenges, including the accurate extraction of change features and the handling of complex and varied image contexts. To address these issues, this study proposes an innovative model named the Segment Anything Model-UNet Change Detection Model (SCDM), which incorporates the proposed center expansion and reduction method (CERM), Segment Anything Model (SAM), UNet, and fine-grained loss function. The global feature map of the environment is extracted, the difference measurement features are extracted, and then the global feature map and the difference measurement features are fused. Finally, a global decoder is constructed to predict the changes of the same region in different periods. Detailed ablation experiments and comparative experiments are conducted on the WHU-CD and LEVIR-CD public datasets to evaluate the performance of the proposed method. At the same time, validation on more complex DTX datasets for scenarios is supplemented. The experimental results demonstrate that compared to traditional fixed-size partitioning methods, the CERM proposed in this study significantly improves the accuracy of SOTA models, including ChangeFormer, ChangerEx, Tiny-CD, BIT, DTCDSCN, and STANet. Additionally, compared with other methods, the SCDM demonstrates superior performance and generalization, showcasing its effectiveness in overcoming the limitations of existing methods. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imaging is essential for monitoring geomorphic changes, urban transformations, and natural disasters. However, the inherent complexities of SAR, particularly pronounced speckle noise, often lead to numerous false detections. To address these challenges, we propose the Multidirectional Attention Fusion Network (MDAF-Net), an advanced framework that significantly enhances image quality and detection accuracy. Firstly, we introduce the Multidirectional Filter (MF), which employs side-window filtering techniques and eight directional filters. This approach supports multidirectional image processing, effectively suppressing speckle noise and precisely preserving edge details. By utilizing deep neural network components, such as average pooling, the MF dynamically adapts to different noise patterns and textures, thereby enhancing image clarity and contrast. Building on this innovation, MDAF-Net integrates multidirectional feature learning with a multiscale self-attention mechanism. This design utilizes local edge information for robust noise suppression and combines global and local contextual data, enhancing the model's contextual understanding and adaptability across various scenarios. Rigorous testing on six SAR datasets demonstrated that MDAF-Net achieves superior detection accuracy compared with other methods. On average, the Kappa coefficient improved by approximately 1.14%, substantially reducing errors and enhancing change detection precision. [ABSTRACT FROM AUTHOR]