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Nowadays, hyperspectral imaging (HSI) attracts the interest of many researchers in solving the remote sensing problems especially in various specific domains such as agriculture, snow/ice, object detection and environmental monitoring. In the previous literature, various attempts have been made to extract the critical information through hyperspectral imaging which is not possible through multispectral imaging (MSI). The classification in image processing is one of the important steps to categorize and label the pixels based on some specific rules. There are various supervised and unsupervised approaches which can be used for classification. Since the past decades, various classifiers have been developed and improved to meet the requirement of remote sensing researchers. However, each method has its own merits and demerits and is not applicable in all scenarios. Past literature also concluded that deep learning classifiers are more preferable as compared to machine learning classifiers due to various advantages such as lesser training time for model generation, handle complex data and lesser user intervention requirements. This paper aims to perform the review on various machine learning and deep learning-based classifiers for HSI classification along with challenges and remedial solution of deep learning with hyperspectral imaging. This work also highlights the various limitations of the classifiers which can be resolved with developments and incorporation of well-defined techniques. [ABSTRACT FROM AUTHOR]

Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model's performance because of the presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art. [ABSTRACT FROM AUTHOR]

Change detection (CD) is rapidly becoming a fundamental approach due to its vast applications in image or video analysis. Siamese convolutional networks (SCN) have recently been used to detect changes. However, these approaches are unable to eliminate drawbacks such as coarse borders and empty holes because of the lack of spatial and other necessary information. So, the main goal of this paper is to develop a network that captures necessary features and has a better feature fusion technique for detecting changes. This is achieved by the proposed Siamese Capsule Multi-level Attention-based CD Network (SCMACDnet). Initially, the Siamese Capsule Encoder–Decoder Network (SCEDnet) is introduced to extract spatial features effectively using capsule attention block (CAB). Afterward, Change Atrous Feature Fusion (CAFF) is introduced to hierarchically combine them by exploring the fusion of channel pairs at several feature levels. Also, multi-scale data from the feature map (FM) is captured with a quicker inference speed using Stride Atrous Convolutional Pooling (SACP) in CAFF. Finally, an effective Reverse Dual Attention (RDA) module is introduced to highlight the changed areas by considering both the channel and pixel-wise correlation (PC) to preserve the shape and edges of the changed areas. Experiments conducted on the CDnet 2014 dataset demonstrate that the SCMACDnet method outperforms other existing methods in terms of precision (.963), sensitivity (.960), the F1 measure (.95), and the Percentage of Wrong Classification (.212). Further, the ablation study validates the efficiency of the proposed SCEDnet, CAFF, and RDA. The results show that the proposed SCMACDnet successfully identifies changes with accurate boundaries and shapes. [ABSTRACT FROM AUTHOR]

Woody vegetation landscape features, such as hedges, tree patches, and riparian vegetation, are important elements of landscape and biotic diversity. For the reason that biodiversity loss is one of the major ecological problems in the EU, it is necessary to establish efficient workflows for the registration and monitoring of woody vegetation landscape features. In the paper, we propose and evaluate a methodology for automated detection of changes in woody vegetation landscape features from a digital orthophoto (DOP). We demonstrate its ability to capture most of the actual changes in the field and thereby provide valuable support for more efficient maintenance of landscape feature layers, which is important for the shaping of future environmental policies. While the most reliable source for vegetation cover mapping is a combination of LiDAR and high-resolution imagery, it can be prohibitively expensive for continuous updates. The DOP from cyclic aerial photography presents an alternative source of up-to-date information for tracking woody vegetation landscape features in-between LiDAR recordings. The proposed methodology uses a segmentation neural network, which is trained with the latest DOP against the last known ground truth as the target. The output is a layer of detected changes, which are validated by the user before being used to update the woody vegetation landscape feature layer. The methodology was tested using the data of a typical traditional Central European cultural landscape, Goričko, in north-eastern Slovenia. The achieved F 1 of per-pixel segmentation was 83.5% and 77.1% for two- and five-year differences between the LiDAR-based reference and the DOP, respectively. The validation of the proposed changes at a minimum area threshold of 100 m2 and a minimum area percentage threshold of 20% showed that the model achieved recall close to 90%. [ABSTRACT FROM AUTHOR]

Damage identification soon after a large-magnitude earthquake is a major problem for early disaster response activities. The faster the damaged areas are identified, the higher the survival chances of inhabitants. Current methods for damage identification are based on the application of artificial intelligence techniques using remote sensing data. Such methods require a large amount of high-quality labeled data for calibration and/or fine-tuning processes, which are expensive in the aftermath of large-scale disasters. In this paper, we propose a novel semi-supervised classification approach for identifying urban changes induced by an earthquake between images recorded at different times. We integrate information from a small set of labeled data with information from ground motion and fragility functions computed on large unlabeled data. A relevant consideration is that ground motion and fragility functions can be computed in real time. The urban changes induced by the 2023 Turkey earthquake sequence are reported as an evaluation of the proposed method. The method was applied to the interferometric coherence computed from C-band synthetic aperture radar images from Sentinel-1. We use only 39 samples labeled as changed and 9000 unlabeled samples. The results show that our method is able to identify changes between images associated with the effects of an earthquake with an accuracy of about 81%. We conclude that the proposed method can rapidly identify affected areas in the aftermath of a large-magnitude earthquake. [ABSTRACT FROM AUTHOR]

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic information about the changes. In this paper, we proposed a hierarchical fusion SAR image change-detection model based on hierarchical fusion conditional random field (HF-CRF). This model introduces multimodal difference images and constructs the fusion energy potential function using dynamic convolutional neural networks and sliding window entropy information. By using an iterative convergence process, the proposed method was able to accurately detect the change-detection regions. We designed a dynamic region convolutional semantic segmentation network with a two-branch structure (D-DRUNet) to accomplish feature fusion and the segmentation of multimodal difference images. The proposed network adopts a dual encoder–single decoder structure where the baseline is the UNet network that utilizes dynamic convolution kernels. D-DRUNet extracts multimodal difference features and completes semantic-level fusion. The Sobel operator is introduced to strengthen the multimodal difference-image boundary information and construct the dynamic fusion pairwise potential function, based on local boundary entropy. Finally, the final change result is stabilized by iterative convergence of the CRF energy potential function. Experimental results demonstrate that the proposed method outperforms existing methods in terms of the overall number of detection errors, and reduces the occurrence of false positives. [ABSTRACT FROM AUTHOR]

Change detection (CD), as a special remote-sensing (RS) segmentation task, faces challenges, including alignment errors and illumination variation, dense small targets, and large background intraclass variance in very high-resolution (VHR) remote-sensing images. Recent methods have avoided the misjudgment caused by illumination variation and alignment errors by increasing the ability of global modeling, but the latter two problems have still not been fully addressed. In this paper, we propose a new CD model called SFCD, which increases the feature extraction capability for small targets by introducing a shifted-window (Swin) transformer. We designed a foreground-aware fusion module to use attention gates to trim low-level feature responses, enabling increased attention to the changed region compared to the background when recovering the changed region, thus reducing background interference. We evaluated our model on two CD datasets, LEVIR-CD and CDD, and obtained F1 scores of 91.78 and 97.87, respectively. The experimental results and visual interpretation show that our model outperforms several previous CD models. In addition, we adjusted the parameters and structure of the standard model to develop a lightweight version that achieves an accuracy beyond most models with only 1.55 M parameters, further validating the effectiveness of our design. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results. [ABSTRACT FROM AUTHOR]

This paper has the main purpose of proposing a methodology to understand the occupation of parking spots by using the synergy of different geomatic techniques. Aerial, satellites, and UAV data are studied through the OBIA to analyse, by change detection, the main differences pre-, during and post-lockdown due to Covid-19. The first results are really promising and pave the ground for a future automation of the proposed procedure. The results can be also integrated in BIM and GIS to help the management of utilities consumption in emergency periods, and they create a dataset to enhance and increase consumption efficiency in residential areas. [ABSTRACT FROM AUTHOR]

Like natural catastrophes or armed conflicts, resource extraction projects herald the alteration or destruction of natural and cultural landscapes alike. Dam construction is a major threat to cultural heritage in Western Asian archaeology. One event may result in obliterating hundreds of sites, most of which never reappear or do so only sporadically following cyclical water fluctuation. Destruction of sites remains ongoing, necessitating constant assessment of damage and the establishment of strategies of documentation and maintenance. This paper proposes a new paradigm for future safeguarding and, more widely, a new tool for managing contiguous terrestrial and lacustrine cultural zones. It outlines a new set of cost-efficient tools for observing these archaeological localities' "emergence patterns" and quickly assessing damage timescales and site areas. As a case study, Iraq's largest hydroelectric basin, the Mosul Dam reservoir on the upper Tigris, is discussed, as it offers several insights into tackling endemic issues of site recording, monitoring, and threat assessment in a constantly shifting environment. The research has profited from an unexpected drought in 2018 yielding archaeological sites and villages thought forever lost and is informed by new archaeological projects recently undertaken on the eastern bank of the lake. [ABSTRACT FROM AUTHOR]

Change detection (CD) for remotely sensed images has gained great relevance in the last decade due to an increase in the number of Earth Observation (EO) missions, improved temporal resolutions, and open data policies. However, efficient exploitation and integration of spatial information for CD remains a challenging issue. This paper proposes a multiscale superpixel segmentation-based band expansion approach to address these issues. Superpixels derived at different scales are used to extract spatial information of objects and landscapes of various sizes, and this information is integrated in the form of new spectral bands. The expanded band set may then be used with any CD method, whether for binary or multi-class CD. Experimental results on multispectral (MS) and hyperspectral (HS) multitemporal images validate the proposed approach's performance benefit. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of change detection, a novel synthetic aperture radar (SAR) image change detection method based on Gamma correction and fuzzy local information c-means clustering (FLICM) model is proposed in this paper. Firstly, the original SAR images are filtered by speckle reducing anisotropic diffusion filter; secondly, the difference image (DI) is obtained by log-ratio operator; thirdly, the DI is processed by the Gamma correction operation; finally, the FLICM model is used to get the change detection result. Experimental results on four groups of SAR images demonstrate that the proposed algorithm has a good performance than many competitive approaches in terms of SAR image change detection. [ABSTRACT FROM AUTHOR]

The loss of archaeological heritage continues today, because of both natural disasters and human-made actions. Alarmingly, a significant amount of the destruction is perpetrated by looters and illegal excavations. This problem is not a new one. However, it has increased exponentially in recent years, especially in countries which witnessed internal turmoil (i.e., the Arab Spring) but also throughout Europe. Local authorities struggle to provide adequate controls because of a lack of human resources, budget constraints or technological know-how. This paper describes a multimodal documentation and monitoring workflow applied to an archaeological site for which, due to the sensitivity of the topic, no specific details can be publicly disclosed. The techniques used include UAV aerial surveys, image-based modelling, change detection, relief visualization and GIS mapping. Thanks to the analysis of the multitemporal datasets, it was possible to assess the extension and spatial progression of illegal excavation over a two-year period. [ABSTRACT FROM AUTHOR]

A novel and effective change detection method based on manifold regularized joint non-negative matrix factorization (MJNMF) framework is proposed in this paper, which detects the changes that occurred in multi-temporal remote sensing images. Most change detection methods, including dictionary learning, principal component analysis (PCA), etc., do not consider the non-negativity among image pixels. However, image itself is a non-negative signal, and the non-negative constraint has better interpretability in practical applications. Nonnegative Matrix Factorization, which incorporates the non-negativity constraint and thus learns object parts, obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. In this paper, our proposed approach based on MJNMF framework aims to establish a pair of joint basis matrices by unchanged training samples from unchanged area. Then, unchanged pixels can be well reconstructed by the corresponding basis matrix, while changed pixels cannot be reconstructed from the basis matrix corresponding to the knowledge of unchanged samples, or a larger reconstruction error can be generated even if changed pixels are reconfigurable. In order to suppress similar information and highlight different information, the cross-reconstruction error is used to generate the difference image. Finally, the binary image is obtained by the robust fuzzy local information c-means (FLICM) clustering algorithm. In addition, inspired by manifold learning, we incorporate manifold regularization into the proposed method to keep the geometric structure of data and improve the accuracy of change detection. Experimental results obtained on simulated and real remote sensing images confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Urban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also because of catastrophic and sudden events, such as earthquakes, landslides, or floods. This paper aims at designing a procedure able to cope with both types of changes by combining interferometric coherence and backscatter amplitude, and provide a semantically meaningful analysis of the changes detected in both city inner cores and suburban areas. Specifically, this paper focuses on detecting multi-dimensional changes in urban areas using a stack of repeat-pass SAR data sets from Sentinel-1A/B satellites. The proposed procedure jointly exploits amplitude and coherence time series to perform this task. SAR amplitude is used to extract changes about the urban extents, i.e. in 2D, while interferometric coherence is sensitive to the presence of buildings and to their size, i. e. to 3D changes. The proposed algorithm is tested using a time-series of two years of Sentinel-1 data, from May 2016 to October 2018, and in two different Chinese cities, Changsha and Hangzhou, with the aim to understand both the temporal evolution of the urban extents, and the changes within what is constantly classified as "urban" throughout the considered time period. [ABSTRACT FROM AUTHOR]

In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can link technological innovation with the conservation and restoration of heritage buildings. This paper presents the ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-disciplinary study of heritage buildings, with the end goal of creating an online HBIM platform for urban monitoring. [ABSTRACT FROM AUTHOR]

In the field of remote sens., change detection is an important monitoring technology. However, effectively extracting the change feature is still a challenge, especially with an unsupervised method. To solve this problem, we proposed an unsupervised transformer boundary autoencoder network (UTBANet) in this paper. UTBANet consists of a transformer structure and spectral attention in the encoder part. In addition to reconstructing hyperspectral images, UTBANet also adds a decoder branch for reconstructing edge information. The designed encoder module is used to extract features. First, the transformer structure is used for extracting the global features. Then, spectral attention can find important feature maps and reduce feature redundancy. Furthermore, UTBANet reconstructs the hyperspectral image and boundary information simultaneously through two decoders, which can improve the ability of the encoder to extract edge features. Our experiments demonstrate that the proposed structure significantly improves the performance of change detection. Moreover, comparative experiments show that our method is superior to most existing unsupervised methods. [ABSTRACT FROM AUTHOR]

In traditional image processing, the Fourier transform is often used to transform an image from the spatial domain to the frequency domain, and frequency filters are designed from the perspective of the frequency domain to sharpen or blur the image. In the field of remote sensing change detection, deep learning is beginning to become a mainstream tool. However, deep learning can still refer to traditional methodological ideas. In this paper, we designed a new convolutional neural network (MFGFNet) in which multiple global filters (GFs) are used to capture more information in the frequency domain, thus sharpening the image boundaries and better preserving the edge information of the change region. In addition, in MFGFNet, we use CNNs to extract multi-scale images to enhance the effects and to better focus on information about changes in different sizes (multi-scale combination module). The multiple pairs of enhancements are fused by the difference method and then convolved and concatenated several times to obtain a better difference fusion effect (feature fusion module). In our experiments, the IOUs of our network for the LEVIR-CD, SYSU, and CDD datasets are 0.8322, 0.6780, and 0.9101, respectively, outperforming the state-of-the-art model and providing a new perspective on change detection. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image change detection is a key technique for such essential applications as flood disaster assessment and forest fire detection. In SAR image change detection, clustering algorithm is the most applied methods, but clustering algorithm only considers the grey features, therefore it is more susceptible to speckle noise. Deep learning model is difficult to be trained by supervised learning because of lack of labels. To alleviate the above-mentioned challenges of SAR image change detection, a parallel dual-branch SAR image change detection network based on clustering and segmentation (Clustering-Segmentation Network) is proposed in this paper. In the clustering branch, the clustering-based change detection results were obtained by fuzzy c-means (FCM) clustering. In the segmentation branch, the Graph-Based Image Segmentation algorithm was used for pre-segmentation. These results were used as labels of the neural network for training for the segmentation-based change detection. After the fusion of these dual-branch, a double sparse dictionary (DSD) discrimination algorithm is proposed to extract the neighbourhood features for the final discrimination, and obtain the final results. By fusing the dual-branch results, the influence of speckle noise on change detection can be suppressed while maintaining a high accuracy. We show that the Clustering-Segmentation Network exhibited better results compared with existing algorithms on several datasets. The accuracy and kappa coefficients are improved by 0.83% and 3.45% respectively, thereby proving the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Change detection (CD) is an important research topic in remote sensing, which has been applied in many fields. In the paper, we focus on the post-processing of difference images (DIs), i.e., how to further improve the quality of a DI after the initial DI is obtained. The importance of DIs for CD problems cannot be overstated, however few methods have been investigated so far for re-processing DIs after their acquisition. In order to improve the DI quality, we propose a global and local graph-based DI-enhancement method (GLGDE) specifically for CD problems; this is a plug-and-play method that can be applied to both homogeneous and heterogeneous CD. GLGDE first segments the multi-temporal images and DIs into superpixels with the same boundaries and then constructs two graphs for the DI with superpixels as vertices: one is the global feature graph that characterizes the association between the similarity relationships of connected vertices in the multi-temporal images and their changing states in a DI, the other is the local spatial graph that exploits the change information and contextual information of the DI. Based on these two graphs, a DI-enhancement model is built, which constrains the enhanced DI to be smooth on both graphs. Therefore, the proposed GLGDE can not only smooth the DI but also correct the it. By solving the minimization model, we can obtain an improved DI. The experimental results and comparisons on different CD tasks with six real datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Since their advent, deep learning (DL) techniques have made great achievement in synthetic aperture radar (SAR) image change detection problem. Yet, their efficiency and accuracy with a presence of speckle noise and limited labeled training data are always full of challenges. To overcome these issues, this paper advocates an innovative approach by exploiting the advantage of the capsule network that can achieve a similar performance with less training samples thanks to its ability to make full use of the relative spatial relationships between features. The proposed multidimensional parallel capsule network (Mdp-CapsNet) includes an attention module to mitigate interference of speckle noise. By focusing more on prominent parts of the feature maps, the attention module guides the network to improve feature extraction. Moreover, a margin-focal loss function is suggested to overcome the influence of imbalanced samples during network optimization. The effectiveness and reliability of the proposed method are demonstrated by comparing its results with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Graffiti, by their very nature, are ephemeral, sometimes even vanishing before creators finish them. This transience is part of graffiti's allure yet signifies the continuous loss of this often disputed form of cultural heritage. To counteract this, graffiti documentation efforts have steadily increased over the past decade. One of the primary challenges in any documentation endeavour is identifying and recording new creations. Image‐based change detection can greatly help in this process, effectuating more comprehensive documentation, less biased digital safeguarding and improved understanding of graffiti. This paper introduces a novel and largely automated image‐based graffiti change detection method. The methodology uses an incremental structure‐from‐motion approach and synthetic cameras to generate co‐registered graffiti images from different areas. These synthetic images are fed into a hybrid change detection pipeline combining a new pixel‐based change detection method with a feature‐based one. The approach was tested on a large and publicly available reference dataset captured along the Donaukanal (Eng. Danube Canal), one of Vienna's graffiti hotspots. With a precision of 87% and a recall of 77%, the results reveal that the proposed change detection workflow can indicate newly added graffiti in a monitored graffiti‐scape, thus supporting a more comprehensive graffiti documentation. [ABSTRACT FROM AUTHOR]

Metropolitan planning has been a dominant trend in spatial development for decades. In this context, Functional Urban Areas (FUAs) have emerged as a concept to illustrate the economic interdependencies between an urban core and its hinterland, being typically determined by commuting patterns or accessibility criteria. In Romania, FUAs were defined for the county capitals (40 cities) as areas where urban development projects could be financed from regional development funds. This paper analyzes the territorial dynamics in one of Romania's fastest-growing functional urban areas, Cluj-Napoca, using a comparative analysis of satellite images and selected indicators. The results demonstrate the spatial impact of economic development in the past 20 years, revealing expanding boundaries of artificial surface and housing units in FUAs. Unfortunately, the current fragmented territorial configuration is unable to manage the new functional relationships generated by real estate dynamics. Therefore, we support the need to introduce a normative territorial plan at the intercommunity level to correlate the spatial development vision between the main city and its fast-growing suburban areas. Such an instrument will not only support an efficient land-use management and enhanced living conditions, but it will also provide a framework to systematically integrate territorial investments around major cities. [ABSTRACT FROM AUTHOR]

The natural terrain (e.g., farm and forest) in temperate zones changes dramatically between seasons due to distinct temperatures and precipitation variations from summer to winter. Moreover, built-up areas vary little in this short period. Therefore, extracting built-up areas via change detection on polarimetric synthetic aperture radar (PolSAR) images is feasible. A common type of PolSAR change detection method is based on hypothesis testing theory. However, in these methods, pixels are selected as the processing units; as a result, these models are computationally complex and poorly maintain the boundaries of built-up areas. In this paper, we innovatively introduce superpixels into the hypothesis test theory and propose a superpixelwise PolSAR change detection method for built-up area extraction. First, we oversegment the PolSAR images into a set of superpixels and derive the probability density function (PDF) of a superpixel's reflectivity on a PolSAR image. Based on this distribution, we present a superpixelwise likelihood-ratio test (LRT) statistic to measure the similarity of two superpixelwise covariance matrices for unsupervised change detection. When actually computing the superpixelwise LRT, the large variation in the areas of the superpixels makes the likelihood functions very complex and estimating the parameters difficult. We further simplify the calculation of the LRT statistic and apply it to built-up area extraction. Compared to the state-of-the-art built-up area extraction methods, our approach provides the best results with overall accuracy values of 91.41%, 92.71%, 93.67% and 93.91% for the four studied areas respectively. In addition, the computational complexity of our method is assessed, and the run time (seconds) of the proposed method in the four study cases is 11.14, 6.32, 5.31, and 4.88, respectively, superior to the values of 66.12, 36.26, 33.65, and 29.67 for DRT, respectively. Code and datasets: https://github.com/SunXJ7/Sentinel-1-Datasets-for-Built-up-Area-Extraction. [ABSTRACT FROM AUTHOR]

Ghana's coastline has been facing erosion and sedimentation phenomena for several decades, resulting in a serious threat to life and property considering that major urban settlements are located on the coast. In this region, there has been a lack of emphasis on comprehensive, large-scale investigations into coastal changes: prior research has predominantly centered on site-specific assessments. These studies have revealed alarming erosion rates, with reports indicating that nearly ten meters are lost annually. The use of high-resolution remotely sensed data can be a consistent support in regions where physical or economic obstacles interfere with collecting in situ information. In particular, the use of continuous all-weather SAR data may facilitate the evaluation of erosion and sedimentation phenomena in coastal areas. In this paper, we apply SAR data over a time period between 2017 and 2021. Sentinel-1 data are pre-processed using the Google Earth Engine platform, and a dedicated algorithm is then applied to identify and quantify erosion and sedimentation processes. Optical images are used as a reference for detecting the location of two areas where consistent sedimentation and erosion phenomena occurred in the considered four years. The results demonstrate that SAR backscattering variations over time offer a reliable method for monitoring coastal changes. This approach enables the identification of the type of phenomena occurring - sedimentation or erosion -, and allows for the quantification of their intensity and dimensions over time. The method can be worldwide applied once the appropriate thresholds are evaluated and help in predictive studies and environmental planning. [ABSTRACT FROM AUTHOR]

Spatiotemporal data are common in practice. Such data often have complicated structures that are difficult to describe by parametric statistical models. Thus, it is often challenging to analyze spatiotemporal data effectively since most existing statistical methods and software packages in the literature are based on parametric modeling and cannot handle certain applications properly. This paper introduces the new R package SpTe2M, which was developed for implementing some recent nonparametric methods for modeling and monitoring spatiotemporal data. This package provides analytic tools for modeling spatiotemporal data nonparametrically and for monitoring dynamic spatial processes sequentially over time. It can be used for different applications, including disease surveillance and environmental monitoring. The use of the package is demonstrated using the Florida influenza-like illness data observed during 2012–2014 and the PM2.5 concentration data in China collected during 2014–2016. [ABSTRACT FROM AUTHOR]

The environment is characterised by subtle and major mutations that cause changes in land use/land cover. Analysis of its dynamics and identification of vulnerable areas are critical to maintaining ecosystem services. The aim of this research is to quantify and qualify land cover dynamics over a 30-year period. It will also highlight forest degradation from a supervised classification of Landsat satellite imagery (L5 TM1987, L7 ETM+ 2000, and L8 OLI/TIRS 2019). The dynamics of land use/land cover were investigated by a maximum likelihood approach using geographic information system (GIS) and remote sensing (RS). Six major land use and land cover (LULC) types were mapped (build-up, agriculture, forest, clearing, matorral and olive cultivation). The classification reports made it possible to assess a reduction in forest cover (from 14 470.11 ha to 5 203.26 ha) and an increase in buildings (from 6 033.69 ha to 9 515.61 ha), and agricultural land (from 9 517.59 ha to 12 338.19 ha). The results were validated by a kappa coefficient of 0.93, 0.91, and 0.96, which showed that the model had successfully predicted LULC changes. We anticipate that the results will provide a basis for decision-making as well as a starting point for further in-depth studies in sustainable management and development of natural resources in the study region. [ABSTRACT FROM AUTHOR]

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Change detection using high-resolution remote sensing images provides crucial information for geospatial monitoring, which is of great importance as urbanization continues. However, current deep learning models for change detection tasks are mostly based on convolutional neural networks (CNNs), from which it is difficult to extract global information owing to the locality of convolution operations. In this paper, we propose a deep learning model, Siam-Swin-UNet (SSUNet), for remote sensing change detection. SSUNet is designed following the classic UNet-like encoder-decoder framework but has three major innovations: (1) The encoder and decoder are pure transformer-based and hierarchically structured, which avoids the locality problem of CNN but retains the capability of hierarchical representation. (2) The encoder incorporates the Siamese structure, which can process bi-temporal remote sensing images in parallel, and to which is added a fusion module to properly fuse the feature maps extracted from the Siamese structure. (3) The backbone of the SSUNet is Swin Transformer V2 blocks, which can be more stable in further applications of the model, such as transfer learning or scaling up of the model capacity. We experimented with the proposed SSUNet on the LEVIR-CD dataset, along with CNN-based models such as UNet, UNet++, FC-Siam-Conc, and FC-Siam-Diff. The results showed our model outperformed the CNN-based models by a large margin based on evaluation metrics including precision, recall, F1-score, and overall accuracy (OA). Moreover, we conducted ablation studies to further prove the effectiveness of the Siamese structure and the choice of the backbone. The proposed SSUNet has great potential for use in remote sensing change detection tasks. [ABSTRACT FROM AUTHOR]

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform's constraints regarding processing time were analyzed by running the detection algorithms on Unibap's iX10-100 space cloud platform. [ABSTRACT FROM AUTHOR]

Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated. [ABSTRACT FROM AUTHOR]

Change detection (CD) is a crucial task in remote sensing (RS) to distinguish surface changes from bitemporal images. Recently, deep learning (DL) based methods have achieved remarkable success for CD. However, the existing methods lack robustness to various kinds of changes in RS images, which suffered from problems of feature misalignment and inefficient supervision. In this paper, a deeply supervised attentive high-resolution network (DSAHRNet) is proposed for remote sensing image change detection. First, we design a spatial-channel attention module to decode change information from bitemporal features. The attention module is able to model spatial-wise and channel-wise contexts. Second, to reduce feature misalignment, the extracted features are refined by stacked convolutional blocks in parallel. Finally, a novel deeply supervised module is introduced to generate more discriminative features. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed DSAHRNet outperforms other state-of-the-art methods, and achieves a great trade-off between performance and complexity. [ABSTRACT FROM AUTHOR]

Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder–decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data. [ABSTRACT FROM AUTHOR]

Remote sensing images change detection (RSICD) is a task to identify desired significant differences between multi-temporal images acquired at different times. From the existing methods, most of them solved this issue with a Siamese network, focusing on how to utilize the comparison between two image features to generate an initial difference map. However, Siamese network-based methods have three drawbacks: (1) complex architecture; (2) rough change map; (3) cumbersome detecting procedure: including feature extraction and feature comparison. To overcome the above drawbacks, we devoted our work to design a general framework which has a simple architecture, integrated detecting procedure, and good capacity of detecting subtle changes. In this paper, we proposed a channel self-attention network based on the generative adversarial network for change detection of remote sensing images. The network used an encoder–decoder network to directly produce a change map from two input images. It was better to detect small punctate and slim linear changes than Siamese-based networks. By regarding RSICD as an image translation problem, we used a Generative Adversarial Network to detect changes. In addition, a channel self-attention module was proposed to further improve the performance of this network. Experimental results on three public remote sensing RGB-image datasets, including change detection dataset, Wuhan University building change detection dataset and LEVIR building Change Detection dataset demonstrated that our method outperformed other state-of-the-art methods. In terms of the F1 score, the proposed method achieved maximum improvements of 5.1%, 3.1%, and 1.7% on the above datasets, respectively. Models and codes will be available at https://github.com/wangle53/CSA-CDGAN. [ABSTRACT FROM AUTHOR]

The variability of surface roughness may lead to relatively large dynamic of backscatter coefficient observed by the synthetic aperture radar (SAR), which complicates the soil moisture (SM) retrieval process based on active remote sensing. The effective roughness parameters are commonly used for parameterizing the soil scattering models, the values of which are often assumed to be constant during different study periods for the same site. This paper investigates the reasonableness of this hypothesis from the perspective of backscatter coefficient simulation and SM retrieval using high resolution SAR data. Three years of Sentinel-1A data from 2016 to 2018 were collected over a sparsely vegetated field within the REMEDHUS SM monitoring network. The advanced integral equation model (AIEM) and Dobson dielectric mixing model were combined for optimizing the effective roughness parameters, as well as simulating the backscatter coefficient and retrieving the SM. The effective roughness parameters were optimized at different temporal periods, such as 2016, 2017, 2018, 2016 + 2017, 2017 + 2018, and 2016 + 2017 + 2018, to analyze their temporal dynamics. It was found that: (1) the effective roughness parameters optimized at different temporal periods are very close to each other; (2) the simulated backscatter from AIEM is consistent with Sentinel-1A observation with root mean square errors (RMSEs) between 1.133 and 1.163 dB and correlation coefficient ® value equals to 0.616; (3) the seasonal dynamics ofin situ SM is well-captured by the retrieved SM with R values floating at 0.685 and RMSEs ranging from 0.049 to 0.052 m3/m3; and (4) inverse of the AIEM with the implementation of effective roughness parameters achieves better performance for SM retrieval than the change detection method. These findings demonstrate that the assumption on the constant effective roughness parameters during the study period of at least three years is reasonable. [ABSTRACT FROM AUTHOR]

With the process of increasing urbanization, there is great significance in obtaining urban change information by applying land cover change detection techniques. However, these existing methods still struggle to achieve convincing performances and are insufficient for practical applications. In this paper, we constructed a new data set, named Wenzhou data set, aiming to detect the land cover changes of Wenzhou City and thus update the urban expanding geographic data. Based on this data set, we provide a new self-attention and convolution fusion network (SCFNet) for the land cover change detection of the Wenzhou data set. The SCFNet is composed of three modules, including backbone (local–global pyramid feature extractor in SLGPNet), self-attention and convolution fusion module (SCFM), and residual refinement module (RRM). The SCFM combines the self-attention mechanism with convolutional layers to acquire a better feature representation. Furthermore, RRM exploits dilated convolutions with different dilation rates to refine more accurate and complete predictions over changed areas. In addition, to explore the performance of existing computational intelligence techniques in application scenarios, we selected six classical and advanced deep learning-based methods for systematic testing and comparison. The extensive experiments on the Wenzhou and Guangzhou data sets demonstrated that our SCFNet obviously outperforms other existing methods. On the Wenzhou data set, the precision, recall and F1-score of our SCFNet are all better than 85%. [ABSTRACT FROM AUTHOR]

Change detection is a fundamental problem in remote sensing image processing. Due to the great advantages in learning the knowledge representations and the complex relationship from large-scale datasets, deep learning has made great progress in change detection tasks in remote sensing community. However, most of the existing methods based on deep learning for change detection are implemented by learning differences of image pairs directly without paying considerations in influences of unstructured and temporal changes, or called nature changes, such as light and seasonal changes. In this paper, an end-to-end deep learning network for remote sensing image change detection is proposed, aiming to accurately detect the change of regions from a high-resolution image pair by learning prototype similarity, in which the metric learning is used and it is one of the meta-learning methods to learn change prototypes from support image pairs. The similarity between the query image pairs and the change prototypes can be measured by a learnable CNN metric. The experimental results based on the two public change detection datasets of high-resolution satellite images, CDD and BCDD, show that our proposed method performs better than other state-of-the-art change detection methods with an improvement of 3.5% and 0.4%, respectively. [ABSTRACT FROM AUTHOR]

This paper considers the real-time detection of abrupt and persistent anomalies in high-dimensional data streams. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time before the system possibly gets harmed. We propose a sequential and multivariate anomaly detection method that scales well to high-dimensional datasets. The proposed method follows a nonparametric, i.e., data-driven, and semi-supervised approach, i.e., trains only on nominal data. Thus, it is applicable to a wide range of applications and data types. Thanks to its multivariate nature, it can quickly and accurately detect challenging anomalies, such as changes in the correlation structure. Its asymptotic optimality and computational complexity are comprehensively analyzed. In conjunction with the detection method, an effective technique for localizing the anomalous data dimensions is also proposed. The practical use of proposed algorithms are demonstrated using synthetic and real data, and in variety of applications including seizure detection, DDoS attack detection, and video surveillance. [ABSTRACT FROM AUTHOR]

Change detection is the foundation of intelligent video surveillance of the airport ground. However, experiments have shown that change detection algorithms with good performance on traditional datasets (e.g., CDnet2014) perform poorly in airport ground surveillance. The reason is that traditional datasets focus on the diversity of scenarios, while the practical application requires robustness against various changes in a single scene. We posit that the solution to this problem is to establish a unique dataset for airport ground surveillance and develop specific algorithms for this scenario. In this paper, we present an Airport Ground Video Surveillance benchmark (AGVS) for change detection of the airport ground. AGVS includes 25 long videos, amounting to about 100000 frames and accurate ground truth for all frames. Each video contains multiple challenges specific to the airport ground (e.g., haze, camouflage, strip shape, shadow and illumination change, simultaneous multi-scale objects) and various appearance changes of the aircraft). Change detection ground truth is generated by manual annotation. The AGVS benchmark can be downloaded from https://www.agvs-caac.com. Furthermore, we conduct a simple review of current change detection algorithms, both unsupervised or supervised, and then 21 state-of-the-art algorithms are tested and analyzed on the AGVS benchmark. Finally, we conclude with algorithm design principles of change detection for airport ground surveillance. [ABSTRACT FROM AUTHOR]

Change detection (CD) with heterogeneous images is currently attracting extensive attention in remote sensing. In order to make heterogeneous images comparable, the image transformation methods transform one image into the domain of another image, which can simultaneously obtain a forward difference map (FDM) and backward difference map (BDM). However, previous methods only fuse the FDM and BDM in the post-processing stage, which cannot fundamentally improve the performance of CD. In this paper, a change alignment-based change detection (CACD) framework for unsupervised heterogeneous CD is proposed to deeply utilize the complementary information of the FDM and BDM in the image transformation process, which enhances the effect of domain transformation, thus improving CD performance. To reduce the dependence of the transformation network on labeled samples, we propose a graph structure-based strategy of generating prior masks to guide the network, which can reduce the influence of changing regions on the transformation network in an unsupervised way. More importantly, based on the fact that the FDM and BDM are representing the same change event, we perform change alignment during the image transformation, which can enhance the image transformation effect and enable FDM and BDM to effectively indicate the real change region. Comparative experiments are conducted with six state-of-the-art methods on five heterogeneous CD datasets, showing that the proposed CACD achieves the best performance with an average overall accuracy (OA) of 95.9 % on different datasets and at least 6.8 % improvement in the kappa coefficient. [ABSTRACT FROM AUTHOR]

In recent years, with the extensive application of deep learning in images, the task of remote sensing image change detection has witnessed a significant improvement. Several excellent methods based on Convolutional Neural Networks and emerging transformer-based methods have achieved impressive accuracy. However, Convolutional Neural Network-based approaches have difficulties in capturing long-range dependencies because of their natural limitations in effective receptive field acquisition unless deeper networks are employed, introducing other drawbacks such as an increased number of parameters and loss of shallow information. The transformer-based methods can effectively learn the relationship between different regions, but the computation is inefficient. Thus, in this paper, a multi-scale feature aggregation via transformer (MFATNet) is proposed for remote sensing image change detection. To obtain a more accurate change map after learning the intra-relationships of feature maps at different scales through the transformer, MFATNet aggregates the multi-scale features. Moreover, the Spatial Semantic Tokenizer (SST) is introduced to obtain refined semantic tokens before feeding into the transformer structure to make it focused on learning more crucial pixel relationships. To fuse low-level features (more fine-grained localization information) and high-level features (more accurate semantic information), and to alleviate the localization and semantic gap between high and low features, the Intra- and Inter-class Channel Attention Module (IICAM) are integrated to further determine more convincing change maps. Extensive experiments are conducted on LEVIR-CD, WHU-CD, and DSIFN-CD datasets. Intersection over union (IoU) of 82.42 and F1 score of 90.36, intersection over union (IoU) of 79.08 and F1 score of 88.31, intersection over union (IoU) of 77.98 and F1 score of 87.62, respectively, are achieved. The experimental results achieved promising performance compared to certain previous state-of-the-art change detection methods. [ABSTRACT FROM AUTHOR]

Effectively utilizing the complementary characteristics between optical and SAR remote sensing images to accurately identify change information is of great practical significance. Direct pixel comparisons are challenging since they come from sensors with different imaging mechanisms. Therefore, in this paper, a novel domain transformation that incorporates channel interaction and a bridging-enhanced (CIBENet) heterogeneous change detection network is proposed, where twin-depthwise separable fused gated channel transformation is the convolutional block designed for channel information interaction. And samples without changed semantic information are used to train the deep transformation model, which effectively solves the interference of semantic information on NiceGAN. A different perspective is provided for the heterogeneous change detection task. The backend change detection network takes UNet++ with twin-depthwise separable convolution as the baseline, introduces the gated channel transformation and bridging-enhanced decoder, and models the feature relationship between channels to strengthen the channel information interaction while suppressing the expression of nonchanged information. In addition, the bridging-enhanced decoder can efficiently solve localized holes and discontinuities in binary maps by bridging identical pixels. CIBENet is supervised and experimented on three heterogeneous change detection datasets, Gloucester, Shuguang, and Italy, also compared with the classical unsupervised methods CGAN, SCCN, INLPG and advanced supervised methods DTCDN, DACDT. The network model proposed in this paper significantly improved F1 and recall, and the overall accuracies were 96.60 %, 98.23 %, and 96.29 %, respectively. The experiments validated the reliability of the proposed model.

The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase remote sensing images. Although synthetic aperture radar (SAR) data have strong potential for application in forest change detection tasks, most existing deep learning-based models have been designed for optical imagery. Therefore, to effectively combine optical and SAR data in forest change detection, this paper proposes a double Siamese branch-based change detection network called DSNUNet. DSNUNet uses two sets of feature branches to extract features from dual-phase optical and SAR images and employs shared weights to combine features into groups. In the proposed DSNUNet, different feature extraction branch widths were used to compensate for a difference in the amount of information between optical and SAR images. The proposed DSNUNet was validated by experiments on the manually annotated forest change detection dataset. According to the obtained results, the proposed method outperformed other change detection methods, achieving an F1-score of 76.40%. In addition, different combinations of width between feature extraction branches were analyzed in this study. The results revealed an optimal performance of the model at initial channel numbers of the optical imaging branch and SAR image branch of 32 and 8, respectively. The prediction results demonstrated the effectiveness of the proposed method in accurately predicting forest changes and suppressing cloud interferences to some extent. [ABSTRACT FROM AUTHOR]

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands. [ABSTRACT FROM AUTHOR]

Satellite earth observation data has become fundamental in efforts to map coastal change at large geographic scales. Research has generally focussed on extracting the instantaneous waterline position from time-series of satellite images to interpret long-term trends. The use of this proxy can, however, be uncertain because the waterline is sensitive to marine conditions and beach gradient. In addition, the technique disregards potentially useful data stored in surrounding pixels. In this paper, we describe a pixel-based technique to analyse coastal change. A hybrid rule-based and machine learning methodology was developed using a combination of Sentinel multispectral and Synthetic Aperture Radar composite imagery. The approach was then used to provide the first national-scale pixel-based landcover classification for the open coast of New Zealand. Nine landcover types were identified including vegetation, rock, and sedimentary classes that are common on beaches (dark sand, light sand, and gravel). Accuracy was assessed at national scale (overall accuracy: 86%) and was greater than 90% when normalised for class area. Using a combination of optical and Synthetic Aperture Radar data improved overall accuracy by 14% and enhanced the separation of coastal sedimentary classes. Comparison against a previous classification approach of sandy coasts indicated improvements of 30% in accuracy. The outputs and code are freely available and open-source providing a new framework for per-pixel coastal landcover mapping for all regions where public earth observation data is available. [ABSTRACT FROM AUTHOR]

Change detection using heterogeneous remote sensing images is an increasingly interesting and very challenging topic. To make the heterogeneous images comparable, some graph-based methods have been proposed, which first construct a graph for the image to capture the structure information and then use the graph to obtain the structural changes between images. Nonetheless, previous graph-based change detection approaches are insufficient in representing and exploiting the image structure. To address these issues, in this paper, we propose an auto-weighted structured graph (AWSG)-based regression method for heterogeneous change detection, which mainly consists of two processes: learning the AWSG to capture the image structure and using the AWSG to perform structure regression to detect changes. In the graph learning process, a self-conducted weighting strategy is employed to make the graph more robust, and the local and global structure information are combined to make the graph more informative. In the structure regression process, we transform one image to the domain of the other image by using the learned AWSG, where the high-order neighbor information hidden in the graph is exploited to obtain a better regression image and change image. Experimental results and comparisons on four real datasets with seven state-of-the-art methods demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]