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Change detection in high-resolution Earth observation is a fundamental Earth vision task to understand the subtle temporal dynamics of Earth's surface, significantly promoted by generic vision technologies in recent years. Vision Transformer is a powerful component to learning spatiotemporal representation but with enormous computation complexity, especially for high-resolution images. Besides, there is still lacking principles in designing macro architectures integrating these advanced vision components for various change detection tasks. In this paper, we present a deep probabilistic change model (DPCM) to provide a unified, interpretable, modular probabilistic change process modeling to address multiple change detection tasks, including binary change detection, one-to-many semantic change detection, and many-to-many semantic change detection. DPCM describes any complex change process as a probabilistic graphical model to provide theoretical evidence for macro architecture design and generic change detection task modeling. We refer to this probabilistic graphical model as the probabilistic change model (PCM), where DPCM is the PCM parameterized by deep neural networks. For parameterization, the PCM is factorized into many easy-to-solve distributions based on task-specific assumptions, and then we can use deep neural modules to parameterize these distributions to solve the change detection problem uniformly. In this way, DPCM has both theoretical macro architecture from PCM and strong representation capability of deep networks. We also present the sparse change Transformer for better parameterization. Inspired by domain knowledge, i.e., the sparsity of change and the local correlation of change, the sparse change Transformer computes self-attention within change regions to model spatiotemporal correlations, which has a quadratic computational complexity of the change region size but independent of image size, significantly reducing computation overhead for high-resolution image change detection. We refer to this instance of DPCM with sparse change Transformer as ChangeSparse to demonstrate their effectiveness. The experiments confirm ChangeSparse's superiority in speed and accuracy for multiple real-world application scenarios, such as disaster response and urban development monitoring. The code is available at https://github.com/Z-Zheng/pytorch-change-models. More resources can be found in http://rsidea.whu.edu.cn/resource_sharing.htm. [ABSTRACT FROM AUTHOR]

Cropland non-agriculturalization (CNA) refers to the conversion of cropland into construction land, woodland/garden/grassland, water body, or other non-agricultural land, which ultimately disrupts local agroecosystems and the cultivation and production of crops. Remote sensing technology is an important tool for large-area CNA detection, and remote sensing based methods that can be used for this task include the time-series analysis method and change detection from bi-temporal images. In particular, change detection methods using high-resolution remote sensing imagery have great potential for CNA detection, but enormous challenges do still remain. The large intra-class variance of cropland with different phenological stages and planting patterns leads to cropland areas being difficult to identify effectively, while certain features can be misidentified because they are similar to cropland, resulting in false alarms and missed detections in the results. There is also a lack of large-scale CNA datasets covering multiple change scenarios as data support. To address these problems, a lightweight model focused on CNA detection (CNANet) is proposed in this paper. Specifically, the uniquely crafted represent-consist-enhance (RCE) module is seamlessly integrated between the encoder and decoder components of CNANet to perform a contrast operation on the deep features extracted by the feature extractor. The RCE module is specifically designed to aggregate multiple cropland representations and extend the cropland representations from the confusing background, to achieve the purpose of reducing the intra-class reflectance differences and enhancing the model's perception of cropland. In addition, a large-scale high-resolution cropland non-agriculturalization (Hi-CNA) dataset was built for the CNA identification task, with a total of 6797 pairs of 512 × 512 images with semantic annotations. Compared to the existing datasets, the Hi-CNA dataset has the advantages of multiple phenological stages, multiple change scenarios, and multiple annotation types, in addition to the large data volume. The experimental results obtained in this study show that the benchmark methods tested on the Hi-CNA dataset can all achieve a good accuracy, proving the high-quality annotation of the dataset. The overall accuracy and F1-score of CNANet with the default settings reach 93.81 % and 78.9 %, respectively, achieving a superior accuracy, compared to the other benchmark methods, and demonstrating stronger perception of cropland changes. In addition, in two selected verification regions within the large-scale real-world CNA mapping results, the F1-score is 83.61 % and 50.87 %. The Hi-CNA can be downloaded from http://rsidea.whu.edu.cn/Hi-CNA_dataset.htm. [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]

In a constant evolving world, change detection is of prime importance to keep updated maps. To better sense areas with complex geometry (urban areas in particular), considering 3D data appears to be an interesting alternative to classical 2D images. In this context, 3D point clouds (PCs), whether obtained through LiDAR or photogrammetric techniques, provide valuable information. While recent studies showed the considerable benefit of using deep learning-based methods to detect and characterize changes into raw 3D PCs, these studies rely on large annotated training data to obtain accurate results. The collection of these annotations are tricky and time-consuming. The availability of unsupervised or weakly supervised approaches is then of prime interest. In this paper, we propose an unsupervised method, called DeepCluster 3D Change Detection (DC3DCD), to detect and categorize multiclass changes at point level. We classify our approach in the unsupervised family given the fact that we extract in a completely unsupervised way a number of clusters associated with potential changes. Let us precise that in the end of the process, the user has only to assign a label to each of these clusters to derive the final change map. Our method builds upon the DeepCluster approach, originally designed for image classification, to handle complex raw 3D PCs and perform change segmentation task. An assessment of the method on both simulated and real public dataset is provided. The proposed method allows to outperform fully-supervised traditional machine learning algorithm and to be competitive with fully-supervised deep learning networks applied on rasterization of 3D PCs with a mean of IoU over classes of change of 57.06% and 66.69% for the simulated and the real datasets, respectively. The code is available at https://github.com/idegelis/torch-points3d-dc3dcd. • DC3DCD, unsupervised learning method for multiclass 3D point cloud change detection. • Revisiting the DeepCluster principle for change segmentation in raw 3D point clouds. • Results are better than classical supervised machine learning methods. • Results are equal to rasterization-based deep learning supervised networks. • In a binary-to-multiclass scenario, DC3DCD competes with fully supervised methods. [ABSTRACT FROM AUTHOR]

In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e.g., optical or radar). Even when considering only optical images, this task has proven to be challenging as soon as the sensors differ by their spatial and/or spectral resolutions. This paper proposes a novel unsupervised change detection method dedicated to images acquired by such so-called heterogeneous optical sensors. It capitalizes on recent advances which formulate the change detection task into a robust fusion framework. Adopting this formulation, the work reported in this paper shows that any off-the-shelf network trained beforehand to fuse optical images of different spatial and/or spectral resolutions can be easily complemented with a network of the same architecture and embedded into an adversarial framework to perform change detection. A comparison with state-of-the-art change detection methods demonstrates the versatility and the effectiveness of the proposed approach. • Detecting changes between images acquired by heterogeneous sensors is formulated as a robust fusion problem. • The method is able to handle multi-band images with possibly different spatial and/or spectral resolutions. • The method is unsupervised, i.e., it does not need a train set composed of image pairs with associated binary change maps. • Any pretrained deep network designed to fuse heterogeneous images can be reused to perform change detection. • When compared to conventional approaches, the strategy appears to be more flexible while competing favorably. [ABSTRACT FROM AUTHOR]

Change detection in remote sensing images aims to accurately determine any significant land surface changes based on acquired multi-temporal image data, being a pivotal task of remote sensing image processing. Over the past few years, owing to its powerful learning and expression ability, deep learning has been widely applied in the general field of image processing and has demonstrated remarkable potentials in performing change detection in images. However, a majority of the existing deep learning-based change detection mechanisms are modified from single-image semantic segmentation algorithms, without considering the temporal information contained within the images, thereby not always appropriate for real-world change detection. This paper proposes a High-Resolution Triplet Network (HRTNet) framework, including a dynamic inception module, to tackle such shortcomings in change detection. First, a novel triplet input network is introduced, which is capable of learning bi-temporal image features, extracting the temporal information reflecting the difference between images over time. Then, a network is employed to extract high-resolution image features, ensuring the learned features preserving high-resolution characteristics with minimal reduction of information. The paper also proposes a novel dynamic inception module, which helps improve the feature expression ability of HRTNet, enriching the multi-scale information of the features extracted. Finally, the distances between feature pairs are measured to generate a high-precision change map. The effectiveness and robustness of HRTNet are verified on three popular high-resolution remote sensing image datasets. Systematic experimental results show that the proposed approach outperforms state-of-the-art change detection methods. [ABSTRACT FROM AUTHOR]

Unsupervised change detection techniques are generally constrained to two multi-band optical images acquired at different times through sensors sharing the same spatial and spectral resolution. In the case of the optical modality, largely studied in the remote sensing community, a straight comparison of homologous pixels such as pixel-wise differencing is suitable. However, in some specific cases such as emergency situations, punctual missions, defense and security, the only available images may be those acquired through different kinds of sensors with different resolutions. Recently some change detection techniques, dealing with images with different spatial and spectral resolutions, have been proposed. Nevertheless, they are focused on a specific scenario where one image has a high spatial and low spectral resolution while the other has a low spatial and high spectral resolution. This paper addresses the problem of detecting changes between any two multi-band optical images disregarding their spatial and spectral resolution disparities. To overcome resolution disparity, state-of-the art methods apply conventional change detection methods after preprocessing steps applied independently on the two images, e.g. resampling operations intended to reach the same spatial and spectral resolutions. Nevertheless, these preprocessing steps may waste relevant information since they do not take into account the strong interplay existing between the two images. Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatially and spectrally degraded versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. Covering the same scene, the latent images are expected to be globally similar except for possible changes in spatially sparse locations. Thus, the change detection task is envisioned through a robust fusion task which enforces the differences between the estimated latent images to be spatially sparse. We show that this robust fusion can be formulated as an inverse problem which is iteratively solved using an alternating minimization strategy. The proposed framework is implemented for an exhaustive list of applicative scenarios and applied to real multi-band optical images. A comparison with state-of-the-art change detection methods evidences the accuracy and the versatility of the proposed robust fusion-based strategy. • Detecting changes between two optical images is formulated as a robust fusion. • The proposed method handles all combination of spatial and spectral image resolutions. • Robust fusion is performed using an alternating minimization algorithm. • All steps of the algorithm boil down to conducting a standard image processing task. • The performance is assessed on a comprehensive set of experiments. [ABSTRACT FROM AUTHOR]

Change detection on multimodal remote sensing images has become an increasingly interesting and challenging topic in the remote sensing community, which can play an essential role in time-sensitive applications, such as disaster response. However, the modal heterogeneity problem makes it difficult to compare the multimodal images directly. This paper proposes a Fourier domain structural relationship analysis framework for unsupervised multimodal change detection (FD-MCD), which exploits both modality-independent local and nonlocal structural relationships. Unlike most existing methods analyzing the structural relationship in the original domain of multimodal images, the three critical parts in the proposed framework are implemented on the (graph) Fourier domain. Firstly, a local frequency consistency metric calculated in the Fourier domain is proposed to determine the local structural difference. Then, the nonlocal structural relationship graphs are constructed for pre-change and post-change images. The two graphs are then transformed to the graph Fourier domain, and high-order vertex information is modeled for each vertex by graph spectral convolution, where the Chebyshev polynomial is applied as the transfer function to pass K-hop local neighborhood vertex information. The nonlocal structural difference map is obtained by comparing the filtered graph representations. Finally, an adaptive fusion method based on frequency-decoupling is designed to effectively fuse the local and nonlocal structural difference maps. Experiments conducted on five real datasets with different modality combinations and change events show the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Historical maps depict past states of the Earth's surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence. [ABSTRACT FROM AUTHOR]

This study is concerned with urban change detection and categorization in point clouds. In such situations, objects are mainly characterized by their vertical axis, and the use of native 3D data such as 3D Point Clouds (PCs) is, in general, preferred to rasterized versions because of significant loss of information implied by any rasterization process. Yet, for obvious practical reasons, most existing studies only focus on 2D images for change detection purpose. In this paper, we propose a method capable of performing change detection directly within 3D data. Despite recent deep learning developments in remote sensing, to the best of our knowledge there is no such method to tackle multi-class change segmentation that directly processes raw 3D PCs. Thereby, based on advances in deep learning for change detection in 2D images and for analysis of 3D point clouds, we propose a deep Siamese KPConv network that deals with raw 3D PCs to perform change detection and categorization in a single step. Experimental results are conducted on synthetic and real data of various kinds (LiDAR, multi-sensors). Tests performed on simulated low density LiDAR and multi-sensor datasets show that our proposed method can obtain up to 80% of mean of IoU over classes of changes, leading to an improvement ranging from 10% to 30% over the state-of-the-art. A similar range of improvements is attainable on real data. Then, we show that pre-training Siamese KPConv on simulated PCs allows us to greatly reduce (more than 3,000 ×) the annotations required on real data. This is a highly significant result to deal with practical scenarios. Finally, an adaptation of Siamese KPConv is realized to deal with change classification at PC scale. Our network overtakes the current state-of-the-art deep learning method by 23% and 15% of mean of IoU when assessed on synthetic and public Change3D datasets, respectively. The code is available at the following link: https://github.com/IdeGelis/torch-points3d-SiameseKPConv. • Siamese KPConv, the first deep network for change segmentation in 3D point clouds. • Urb3DCD–V2, a new simulated dataset to assess 3D point cloud change detection methods. • Pre-training Siamese KPConv on simulated data highly boosts the training on real data. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS) has gained prominence as a deformation monitoring technology in various fields such as bridges and tunnels. This paper offers a comprehensive overview of TLS-based deformation monitoring, covering measurement principles, stochastic models, registration, change detection, and practical applications. We systematically classify, detail, and compare methods for change detection, registration, and georeferencing, shedding light on their strengths, weaknesses, and applicability. The paper also provides an extensive comparison of deformation detection techniques for structures like bridges and tunnels, as well as ground deformation in landslides and unstable rock masses. Challenges related to achieving high-precision, reliable, and efficient deformation monitoring are discussed, and avenues for future research are outlined. Emphasis is placed on the importance of measurement planning to ensure data accuracy and support informed decision-making, particularly for large or spatially constrained structures. Future research should explore leveraging geometric constraints for more accurate change detection and enhancing deformation analysis reliability. • TLS-based deformation monitoring shows commonalities and specific characteristics across scenarios. • Multi-epoch point cloud comparison is essential for all types of deformation monitoring applications. • Measurement planning is crucial for accurate and reliable monitoring in complex structures. • Future research should leverage geometric constraints for precise analysis of deformation. • Research is needed to accurately quantify deformations in monitoring irregular ground surfaces. [ABSTRACT FROM AUTHOR]

• A novel deep learning model for laser-induced damage change detection is proposed. • The proposed model combines siamese CNN with visual attention mechanism. • A novel SoftmaxFocal loss is put forward to train the network. • Our model outperforms four different methods overall on three real datasets. With high-energy laser irradiating, the laser-induced damages may occur in the surfaces of optical elements in laser facilities. As the laser-induced damage changes can badly affect regular and healthy operation of laser facilities, it is essential to effectively detect real damage changes while suppressing meaningless and spurious changes in captured optical images. In order to achieve high-precision laser-induced damage change detection, this paper presents a novel deep learning model which exploits visual attention-based siamese convolutional neural network with SoftmaxFocal loss and significantly improves the performance of damage change detection. In the proposed model, an end-to-end classification network is designed and trained which fuses the spatial-channel domain collaborative attention modules into siamese convolutional neural network thus achieving more efficient feature extraction and representation. For the purpose of addressing the unbalanced distribution of hard and easy samples, a novel loss function which is termed as SoftmaxFocal loss is put forward to train the proposed network. The SoftmaxFocal loss creatively introduces an additive focusing term into original softmax loss which greatly enhances the online hard sample mining ability of the proposed model. Experiments conducted on three real datasets demonstrate the validity and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

[Display omitted] Synthetic aperture radar (SAR) image change detection is a challenging task due to inherent speckle noise, imbalanced class occurrence and the requirement for discriminative feature learning. The traditional handcrafted feature extraction and current convolution-based deep learning techniques have some advantages, but suffer from being limited to neighborhood-based spatial information. The nonlocally observable imbalance phenomenon that exists naturally in small area change detection has presented a huge challenge to methods that focus on local features only. In this paper, an unsupervised method based on a variational graph auto-encoder (VGAE) network was developed for object-based small area change detection using SAR images, with the advantages of alleviating the negative impact of class imbalance and suppressing speckle noise. The main steps include: 1) Three types of difference image (DI) are combined to establish a three-channel fused DI (TCFDI), which lays the data-level foundation for subsequent analysis. 2) Simple linear iterative clustering (SLIC) is used to divide the TCFDI into superpixels regarded as nodes. Two functions are proposed and developed to measure the similarity between nodes to build a weighted undirected graph. 3) A VGAE network is designed and trained using the graph and nodes, and high-level nonlocal feature representations of each node are extracted. The network, with a Gaussian Radial Basis Function constrained by geospatial distances, establishes the connection among nonlocal, but similar superpixels in the process of feature learning, which leads to speckle noise suppression and distinguishable features learned in latent space. The nodes are then identified as changed or unchanged classes via k -means clustering. Five real SAR datasets were used in comparative experiments. Up to 99.72% accuracy was achieved, which is superior to state-of-the-art methods that pay attention only to local information, thus, demonstrating the effectiveness and robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, Convolutional Neural Networks (CNNs) have promoted the prosperity of Change Detection (CD). However, due to the intrinsic property of convolution kernel, this method cannot effectively model the long-distance dependency. The emergence of Vision Transformer (ViT) brings a new way to solve the problem. Based on ViT architecture, a novel Multi-Granularity remote sensing image Change Detection model (MGCDT) is proposed in this paper. We cascade several Local–Global Siamese Transformer (LGST) as backbone to extract local and global semantic discriminative features. In order to solve the serious problem of false detection and missing detection of feature boundary, a plug-and-play High Frequency Enhancement Unit (HFE) is proposed to replace the inflexible U-shaped structure to optimize the detection boundary. Considering the problem of multi-scale modeling of ground objects, a Multi-Scale Fusion Attention Unit (MSFA) is proposed, which integrates the flow of multi-scale information into the calculation process of self-attention. Finally, we utilize a Deep Feature Guidance Unit (DFG) to optimize the shallow detailed feature information. Extensive experiments show that, considering multi-granularity information, MGCDT outperforms the existing change detection algorithms on four remote sensing image change detection datasets. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing data fusion technology, heterogeneous remote sensing image (HRSI) change detection (CD) has become a frontier field. The powerful nonlinear information processing capability of deep neural network provides the possibility for image domain conversion of HRSIs. However, the majority of existing methods rely on stacking network layers to extract deep high-level semantic features to accomplish the mutual conversion of image domains. The effective extraction and utilization of shallow features have not been fully considered. At the same time, the dependence between deep and shallow features has not been deeply explored. Therefore, these algorithms severely restrict the performance of CD. A new HRSI-CD network (DMF2Net) based on multi-level feature fusion is proposed in this paper to address these issues. DMF2Net uses central difference convolution to extract fine-grained features from shallow layers of images. It is capable of capturing the intrinsic detail features of image by aggregating intensity and gradient information. The dynamic multi-level feature fusion method is used to learn the fusion weights from the features, which are then used to guide the organic fusion of shallow and deep semantic features. This can preserve more position and detail features of the image, which can prevent the loss of small data during image conversion and enhance the model's ability to detect subtle changes. On this basis, a new DMF2Net-based method for detecting change in bi-temporal HRSIs is proposed. We conducted extensive experiments on four publicly available HRSI-CD datasets. The experimental results showed that the proposed CD framework achieves significant improvement over the most advanced methods. The project file of the proposed framework will be obtained from https://github.com/cwlnnu/DMF2Net. [ABSTRACT FROM AUTHOR]

Change detection using remote sensing images captured by small unmanned aerial vehicles (small UAVs) finds wide applications across various fields. However, there is a challenge when dealing with images captured at the same location by small UAVs at different times, leading to differences in viewpoint. These viewpoint differences present a significant challenge for most change detection methods. In this paper, we propose an end-to-end network, OFACD, designed to simultaneously address the issues of image alignment and change detection. Our network aligns feature maps using estimated optical flow and performs change detection concurrently. This approach enables the network to directly process images with viewpoint differences, effectively improving performance in scenarios with accumulated errors or large viewpoint variations, as well as enhancing throughput by eliminating repetitive feature extraction. Additionally, to fill the gap of the absence of change detection datasets with viewpoint differences and to evaluate our model, we created two change detection datasets with viewpoint differences. Extensive experimental results demonstrate that our method outperforms several state-of-the-art change detection methods in datasets involving viewpoint differences, exhibiting superior throughput and performance. • A novel change detection model for images with viewpoint differences is proposed. • OFACD simultaneously aligns images and detects changes toenhanceperformance. • Created two change detection datasets with viewpoint differences. • Extensive experimental results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract With the remarkable progress in access to remote sensing imagery data, nowadays research very often utilizes more than one image. We are often able to use multitemporal, hyperspectral, and/or full polarization of microwave radar images. In addition, it has become the general consensus that texture analysis plays an important role in remote sensing. It has been found in several publications that texture analysis was applied to each layer separately; however, this procedure requires a significant amount of computation and produces a massive volume of data. One alternative, and perhaps a better procedure, is to arrange the images into a multi-layered structure and perform texture analysis within some sort of three-dimensional domain. This manuscript extends the concepts of the gray level of co-occurrence matrix (GLCM) texture analysis applied for a single image to a multi-layered set of images, referred to in this paper as 3DGLCM. We then presented an interpretation of the 3DGLCM within the context of building damage identification. A set of 3DGLCM-based features were computed and evaluated as well. As a result, it was observed that some texture features have certain similarities with other methods proposed in previous studies, whereas other features have not been used before. Furthermore, this paper evaluates the performance of the Support Vector Machine (SVM) classifier in learning and detecting collapsed buildings using 3DGLCM-based features. Thus, the empirical evaluation focuses on the identification of collapsed buildings caused by the 2011 Tohoku earthquake and tsunami, where individual polarized TerraSAR-X intensity images are used to compute the texture features, and the collapsed buildings caused by the 2016 Kumamoto earthquake, where LIDAR-based digital surface models are used to compute the texture features. Extensive datasets consisting of building damage states that have been visually inspected by local authorities and research teams are used to set up the training and testing subsets. Furthermore, the proposed texture features are compared with features commonly used to identify collapsed buildings. The study concludes that an SVM trained with 3DGLCM-based features identifies collapsed buildings with high accuracy and outperforms an SVM trained with common features used in previous studies. [ABSTRACT FROM AUTHOR]

The change detection task plays an irreplaceable role in the remote sensing field. However, most methods ignore the edge distinctive information. Such information is not only significant in some change detection tasks such as channel and river changes, but also important for refining the accuracy of change detection. Therefore, an edge refinement multi-feature (ERMF) extraction method, employing a siamese network to extract the primary discriminative features at five scales of bitemporal remote sensing images, is proposed in this paper. On the one hand, an edge refinement module is designed to obtain the edge change map as well as the final accurate region change map. On the other hand, a multi-level feature extraction module is introduced to acquire a coarse change map consisting of low-level location information and high-level semantic information at five different scales. Besides, it is worth emphasizing that we present a hybrid loss to evaluate the ERMF model. Experiments demonstrate that the ERMF model outperforms seven state-of-the-art methods in both qualitative and quantitative evaluations. • In this paper, a novel end-to-end remote sensing change detection model ERMF is proposed, which extracts edge change information and region change information. The results of regional change based on edge change information improved in the objective index, and boundary division is obvious in subjective evaluation. • Aiming at getting edge change information maps and refining the final results, an edge refinement module (ERM) is introduced. • In addition, a decent hybrid loss function is designed to constrain the results of edge change detection and region change detection. [ABSTRACT FROM AUTHOR]

Let X = (X t) t ≥ 0 be a process behaving as a general increasing Lévy process (subordinator) prior to hitting a given unknown level m 0 , then behaving as another different subordinator once this threshold is crossed. This paper addresses the detection of this unknown threshold m 0 ∈ [ 0 , + ∞ ] from an observed trajectory of the process. These kind of model and issue are encountered in many areas such as reliability and quality control in degradation problems. More precisely, we construct, from a sample path and for each ε > 0 , a so-called detection level L ε by considering a CUSUM inspired procedure. Under mild assumptions, this level is such that, while m 0 is infinite (i.e. when no changes occur), its expectation E ∞ (L ε) tends to + ∞ as ε tends to 0, and the expected overshoot E m 0 ( [ L ε − m 0 ] +) , while the threshold m 0 is finite, is negligible compared to E ∞ (L ε) as ε tends to 0. Numerical illustrations are provided when the Lévy processes are gamma processes with different shape parameters. [ABSTRACT FROM AUTHOR]

Co-seismic landslide mapping after earthquake event is essential for emergency rescue, geohazard prevention, and post-disaster reconstruction. Most co-seismic landslide mapping is primarily achieved via field surveys or visual interpretation of remote sensing images. However, such methods are highly labor-intensive and time-consuming, particularly over large areas. This paper proposed an automated co-seismic landslide mapping approach, which has three main advantages compared to state-of-the-art methods. First, it removes the dependence on the manual labeling for training samples through an unsupervised change detection process. Second, the approach takes the lead in introducing multi-scale extended morphological profiles to comprehensively describe the characteristics of various landslides induced by earthquake. Third, it is also the first attempt to employ ensemble strategy in landslide identification, which integrates the advantages of different machine learning-based classifiers, further improving the accuracy of recognition. Three experiments were carried out through multi-temporal Sentinel-2 and PlanetScope images acquired in China and Haiti. The results demonstrated the effectiveness and superiority of the proposed approach compared to other methods, providing an effective solution for complicated co-seismic landslide mapping task in the future. [ABSTRACT FROM AUTHOR]

Ecocultural restoration involves the reciprocal repair of ecosystems and revitalization of cultural practices to enhance their mutual resilience to natural and anthropogenic disturbances and climate change stressors. Resilient ecocultural systems are adapted to retain structure and function in the face of disturbances that remain within historical ranges of severity. To assist in ecocultural restoration and management, understanding how a system has historically responded to different types of disturbances is therefore invaluable in understanding how social-ecological resilience can be maintained in the face of future stressors and disturbances. However, records of disturbances and ecocultural responses can be limited for certain landscapes and human communities. In this methods paper, we demonstrate a mixed-method process for integrating oral history, field-based knowledge, archival information, and historical and contemporary aerial images to gain insight into the changes on the Klamath River in Northern California from the 1940s through 2020. We georegistered historical imagery, quantified changes between land cover classes, and contextualized these classifications with qualitative assessments of changes in larger surrounding areas. By synthesizing these data sources with field measurements, mining and other land survey maps, timber management plans, fire and flood histories, and interviews with members of the Karuk Tribe, we were able to reconstruct the land use and land cover change histories at five sites. We noted that recovery of canopy cover from fire and logging practices was faster than for flood, which was faster than recovery from mining, consistent with the relative severity of likely soil disturbance. By combining different sources of information with complementary strengths, we were able to provide managers with site-specific information on recovery from different types of disturbance. Though this approach was labor-intensive, with emerging tools for supervised classification of high-resolution imagery, mixed-method analytical historical ecology could be applied more broadly, supporting ecocultural restoration on a larger scale. [Display omitted] • Reconstructing landscape histories helps understand ecosystem response to disturbance. • Combining qualitative and quantitative analysis helps reconstruct land-use. • Synthesizing many historical data sources improves ecocultural restoration planning. • Manual classification could improve with machine learning 'human-in-the-loop' tools. [ABSTRACT FROM AUTHOR]

• A change detection method is proposed with heterogeneous remote sensing images. • It takes advantage of Cycle-Consistent Adversarial Networks for data mining. • Copula mixture is constructed to model the dependence structure of the images. • Experiments on real datasets validate the superiority of the proposed method. In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Change detection is the amount of changes that can guide to more concrete understandings into essential method concerning land cover, land usage and ecological variations. This paper deals with an intelligent methodology to optimize the solution out of the solution space that improves the efficiency of the change detection process. To support the theme, an integrated semi-supervised method is designed with a focus on image fusion, semi supervised clustering and binary swarm based optimization. A new approach of handling fusion using sparse coding is referred to expand the amount of information. Using the extracted information, change detection process is carried out by a constrained clustering technique to provide a solution reflecting the level of changes occurred in the region of investigation. To improve the accuracy by proposing a global optimum solution, still the result is refined through binary swarm based optimization process and hence the results are accelerated towards the increased level of accuracy followed by which the change map is reconstructed to show case changes prominently. To determine the accurateness of the proposed methodology quantitative and qualitative analysis has been done with different datasets. The proposed method has been evaluated with existing techniques such as k-means, AKM, FCM, ECKM and ASCC to show the efficiency and proved to be the preeminent change detection methodology compared to the state-of-art methods. [ABSTRACT FROM AUTHOR]

Multi-temporal high spatial resolution earth observation makes it possible to detect complex urban land surface changes, which is a significant and challenging task in remote sensing communities. Previous works mainly focus on binary change detection (BCD) based on modern technologies, e.g., deep fully convolutional network (FCN), whereas the deep network architecture for semantic change detection (SCD) is insufficiently explored in current literature. In this paper, we propose a deep multi-task encoder-transformer-decoder architecture (ChangeMask) designed by exploring two important inductive biases: sematic-change causal relationship and temporal symmetry. ChangeMask decouples the SCD into a temporal-wise semantic segmentation and a BCD, and then integrates these two tasks into a general encoder-transformer-decoder framework. In the encoder part, we design a semantic-aware encoder to model the semantic-change causal relationship. This encoder is only used to learn semantic representation and then learn change representation from semantic representation via a later transformer module. In this way, change representation can constrain semantic representation during training, which introduces a regularization to reduce the risk of overfitting. To learn a robust change representation from semantic representation, we propose a temporal-symmetric transformer (TST) to guarantee temporal symmetry for change representation and keep it discriminative. Based on the above semantic representation and change representation, we adopt simple multi-task decoders to output semantic change map. Benefiting from the differentiable building blocks, ChangeMask can be trained by a multi-task loss function, which significantly simplifies the whole pipeline of applying ChangeMask. The comprehensive experimental results on two large-scale SCD datasets confirm the effectiveness and superiority of ChangeMask in SCD. Besides, to demonstrate the potential value in real-world applications, e.g., automatic urban analysis and decision-making, we deploy the ChangeMask to map a large geographic area covering 30 km2 with 300 million pixels. Code will be made available. [ABSTRACT FROM AUTHOR]

The coastline of the Nile Delta is a dynamic zone in Egypt that is received considerable attention. This region contains many major cities and it also contains a number of economically important harbors such as Damietta, Port Said and Alexandria. This research focuses attention on applying Geographical Information System (GIS) tool to assess prospective changes along the coast of the Nile Delta, Egypt during the past thirty-four years. In this paper, changes to the coastline were monitored using twelve scenes of Landsat images from 1984 to 2018. After the extraction of shorelines from Landsat images, Digital Shoreline Analysis System (DSAS) was used to calculate shoreline displacement rate. The results showed that about 44.2% of the study area was under erosion and 45% was under accretion while it was stable at 10.8% between 1984 and 2018. The results illustrated that Rosetta and Damietta promontories are displayed higher rates of erosion than other coastal regions of the Nile Delta. National measures must be implemented to protect the entire coast in threatened areas. [ABSTRACT FROM AUTHOR]

Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth's resources in an efficient manner. An important factor in climate change is the change in the green cover. Non-availability of standard datasets and limited labeled data points makes it difficult to attain high accuracies in change detection. In this paper, we have proposed a modified ensemble of extreme learning machines (mAEELM) for change detection in green cover to increase the accuracy of the change detection process. It uses Extreme Learning Machine (ELM) as the base classifier. Different ELMs are trained with different configuration so that they have different learning capabilities. These ELMs are combined to create an ensemble and it is then adapted based on the accuracy of the individual ELMs. The ensemble is then pruned to eliminate the ELMs which are not contributing towards the overall result of the ensemble, to make it more efficient. The proposed algorithm has been applied for detecting change on two areas of Gandhuan, Punjab and Chaparkaura Kham, Uttar Pradesh, India. The algorithm shows an average accuracy of 97.8% on both the datasets. [ABSTRACT FROM AUTHOR]

• Instead of supervised methods, unsupervised adversarial learning is proposed to construct spectral mapping network for HS change detection, which not only breaks the dilemma of limited training datasets, but also opens up a more flexible way for HS change detection. • A new spectral distance constraint is enforced on the spectral mapping network to enhance the adaptability of the network to change detection tasks. Specifically, it is enforced the reconstruction space spectral constraint loss and latent space adversarial loss to acquire a better spectral reconstruction effect and make the deep latent space obey multivariate Gaussian distribution. • Considering that the changed targets often exist in connected pixel areas rather than several isolated pixels, we pay attention to the spatial correlation to design the spatial attribute optimization, thereby significantly improving our UHCD framework performance. Unlike the existing change detection approaches based on the multispectral (MS) image and synthetic aperture radar (SAR) image datasets, a novel unsupervised hyperspectral change detection (UHCD) framework is proposed in this paper. The UHCD framework is designed for hyperspectral images with high dimensions and low availability. This framework consists of two modules: the spectral mapping with adversarial learning and the discriminant analysis with spatial attribute optimization. In comparison with other advanced change detection methods, the proposed framework possesses three distinctive properties: (1) The unsupervised spectral mapping is leveraged to exploit underlying spectral features without the requirement of pseudo-training datasets in the change detection task; (2) We introduce spectral constraint loss into reconstruction space and adversarial loss into latent space to enhance the quality of the features extracted by the spectral mapping network; (3) Spatial attribute optimization uses the spatial correlation to further improve the performance of the proposed UHCD method. The experimental results on two real datasets show that the proposed UHCD achieves competitive performance. [ABSTRACT FROM AUTHOR]

With strong penetrability and high resolution, synthetic aperture radar (SAR) images have been widely used in remote sensing image change detection. With the essence of heuristics, metaheuristics are suitable for the needs of most real-life optimization problems according to the expected solution quality and allowable calculation time. To improve the accuracy of change detection, this paper proposes a novel efficient metaheuristic change detection procedure. More specifically, we develop a superpixel-based multiobjective change detection method based on superpixel-wise feature representation and self-adaptive neighborhood-based binary difference evolution. During the clustering for feature analysis, a multiobjective optimization problem (MOP) is modeled which take the likelihood and Bhattacharyya distance between changed and unchanged classes as two objective functions. To solve the MOP, this paper firstly applies the superpixel binary representation technique into the encoding process of multiobjective evolutionary algorithm so that the dimension of pixel coding space can be substantially reduced. Then a self-adaptive neighborhood-based binary differential evolution strategy is proposed to explore the optimal change detection strategy, where a novel mutation operator integrating neighborhood information is designed for the improvement of change detection performance. Experimental results on three real SAR datasets confirm that the proposed method can make a better performance on change detection and the metaheuristic algorithm has good convergence and stability. • Change detection of synthetic aperture radar images. • Superpixel-based difference feature representation strategy. • Difference analysis in a form of evolutionary multi-objective optimization. • A self-adaptive neighborhood-based binary difference evolution framework. [ABSTRACT FROM AUTHOR]

This paper proposes a change detection algorithm based on Fully Convolutional Siamese Networks for optical aerial images, which is trained by using an improved Contrastive Loss-Focal Contrastive Loss (FCL). The proposed framework equipped with contrastive loss can extract features directly from image pairs and measure changes by using a distance metric. In other words, this method encourages reducing intra-class variance and enlarging inter-class difference, so that the binarized change map can be obtained by a simple threshold. In change detection task, a critical problem is how to overcome example imbalance (i.e. unchanged examples are much more than changed examples). To address this challenge, a novel focal contrastive loss is proposed to further improve the performance of the model. FCL can reduce the impact of example imbalance and make the model focus learning on hard examples. Extensive experiments demonstrate that the proposed approach is more abstract as well as robust. Compared with other baseline methods, the presented method achieves better results on SAZDA, TISZADOB, CDD, and WHU-CD data set. It achieves state-of-the-art performance in terms of the F1 measure. [ABSTRACT FROM AUTHOR]

Change detection plays a crucial role in observing earth surface transition and has been widely investigated using deep learning methods. However, the current deep learning methods for pixel-wise change detection still suffer from limited accuracy, mainly due to their insufficient feature extraction and context aggregation. To address this limitation, we propose a novel Cross Layer convolutional neural Network (CLNet) in this paper, where the UNet structure is used as the backbone and newly designed Cross Layer Blocks (CLBs) are embedded to incorporate the multi-scale features and multi-level context information. The designed CLB starts with one input and then split into two parallel but asymmetric branches, which are leveraged to extract the multi-scale features by using different strides; and the feature maps, which come from the opposite branches but have the same size, are concatenated to incorporate multi-level context information. The designed CLBs aggregate the multi-scale features and multi-level context information so that the proposed CLNet can reuse extracted feature information and capture accurate pixel-wise change in complex scenes. Quantitative and qualitative experiments were conducted on a public very-high-resolution satellite image dataset (VHR-Dataset), a newly released building change detection dataset (LEVIR-CD Dataset) and an aerial building change detection dataset (WHU Building Dataset). The CLNet reached an F1-score of 0.921 and an overall accuracy of 98.1% with the VHR-Dataset, an F1-score of 0.900 and an overall accuracy of 98.9% with the LEVIR-CD Dataset, and an F1-score of 0.963 and an overall accuracy of 99.7% with the WHU Building Dataset. The experimental results with all the selected datasets showed that the proposed CLNet outperformed several state-of-the-art (SOTA) methods and achieved competitive accuracy and efficiency trade-offs. The code of CLNet will be released soon at: https://skyearth.org/publication/project/CLNet. [ABSTRACT FROM AUTHOR]

• Analyzed areas of tree removal along interstates in Georgia, USA with NAIP imagery. • 25% of previously forested interstate roadsides to have experienced tree removal. • Tree crashes were not reduced despite this program goal. • Agencies need to consider measures other than tree removal to prevent tree crashes. Transport agencies worldwide remove roadside trees in the name of traffic safety. In 2017, the Georgia Department of Transportation (GDOT), the agency managing state and federal highways in the US state of Georgia, began clear-cutting trees on all transport agency property statewide, amounting to thousands of hectares of cleared land. As justification, GDOT cited fatal traffic crashes with trees. In this paper, we quantified the extent of tree removal within a 60 m buffer along the five major interstates in Georgia, using aerial imagery from the United States Department of Agriculture (USDA) Farm Service Agency's National Agricultural Imagery Project (NAIP). We used supervised image classification and change detection on aerial imagery between 2015 and 2021. We also collected pre- and post-treatment crash data and conducted a difference-in-differences analysis on sampled road segments to isolate the effect of tree removal on crash rates. Results showed that 28.4 sq km of previously forested land in the interstate corridors were cleared of trees, 25% of the total forested land in the corridors. Tree crash fatalities did not decrease. Findings provide insight into approaches to roadside safety like tree removal while helping assess impacts and plan for next steps. The lack of evidence for reduced tree crash fatalities indicate that transport agencies should consider measures other than large-scale tree removal to prevent fatal crashes with trees. [ABSTRACT FROM AUTHOR]

Cyber-resilience is an increasing concern for autonomous navigation of marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges: multiple sensor information fusion, diagnosis of not-normal behaviours, and change detection. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for coastal navigation. Developing a Likelihood Field approach, the first stage extracts shoreline features from radar and matches them to the electronic navigation chart. The second stage associates buoy and beacon features from the radar with chart information. Using real data logged at sea tests combined with simulated spoofing, the paper verifies the ability to timely diagnose and isolate an attempt to compromise position measurements. A new approach is suggested for high level processing of received data to evaluate their consistency, which is agnostic to the underlying technology of the individual sensory input. A combined generalized likelihood ratio test using both parametric Gaussian modelling and Kernel Density Estimation is suggested and compared with a detector using only either of two. The paper shows how the detection of deviations from nominal behaviour is possible when the navigation sensor is under attack or defects occur. • Erroneous information from navigation sensors is identified and mitigated as follows. • A two-stage estimator is designed for coastal navigation to validate Radar and Gyro. • Radar and ENC shoreline features are first matched in a Likelihood Field approach. • Thereafter, buoy and beacon features are matched to isolate defects in information. • Parametric and non-parametric models are employed for efficient GLRT change detection. • Simulating spoofing attacks as overlay on sea test data demonstrates resilience. [ABSTRACT FROM AUTHOR]

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as “targeted change detection”. Based on a one-class classifier “Support Vector Domain Description (SVDD)”, a novel algorithm named “Three-layer SVDD Fusion (TLSF)” is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison. [ABSTRACT FROM AUTHOR]

• A multiscale superpixel reconstruction method was developed to generate the Difference Image. • A two-stage center-constrained FCM algorithm was designed to deal with imbalanced data clustering. • CWNN combined with DCGAN was adopted to classify hard pixels. Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c -means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. [ABSTRACT FROM AUTHOR]

Most of the previous change detection methods are designed based on the difference of two images. However, directly using intensity or the features to generate difference image may be easily affected by the illumination and camera pose variations. In this paper, we show that accurate change detection results can be obtained by fusing the absolute difference of multiscale deep features of the reference and query images. Specifically, we build a change detection network, which computes absolute difference of the multiscale deep features of image pairs and learns adaptive features for change detection. The proposed network is based on off-the-shelf CNNs, whose convolutional layer blocks are used as feature extracting modules to extract multiscale deep features. We devise intra and cross encoding modules. The intra encoding modules are used for learning change related features from extracted features. These features are used for generating absolute difference features (ADFs). By progressively fusing the ADFs from high to low layers with cross encoding modules, we obtain full resolution of change detection result. Extensive experiments on three change detection benchmark datasets validate the superiority and effectiveness of the proposed method over the state-of-the-art change detection methods. [ABSTRACT FROM AUTHOR]

Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically. This paper introduces the open-access Python 3 package Pyeo - " Py thon for E arth O bservation". Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences. • Highlight 1: A Python package for Earth observation processing chains for change detection is presented. • Highlight 2: Data can be processed in near-real-time whenever a new satellite image is acquired. • Highlight 3: The satellite change detection algorithm informs the user of detected change events. • Highlight 4: An application to a forest in Kenya is presented to demonstrate the software. • Highlight 5: This software is used by the Kenya Forest Service for monitoring deforestation from Sentinel-2. [ABSTRACT FROM AUTHOR]

This paper presents a camera-projector system which allows for 3D reconstruction and copyright protection. Our approach relies on video-embedded binary patterns whose proportion of black and white at each pixel correspond to the original grayscale value. When projected at a sufficiently high frame rate, these binary patterns are seamless to the observer and look as if a normal video was projected. These patterns have been designed to encode pixel position based on a collection of on-off/off-on transitions. When the camera is properly adjusted, these temporal transitions can be detected with a change detection method and converted into a 3D surface. However, when filmed by an uncalibrated camera, these transitions are not only unreadable, they induce spatial parasitic patterns in the recorded video. Our method is motivated by two families of applications. First, all applications for which a creative visual work needs to be pre-warped according to a 3D model to prevent geometric distortion when displayed on a dynamic scene. Typical examples include augmented reality and plays involving artistic staging. Second, all applications for which a multimedia copyrighted document (e.g. a movie projected in a theater) shall not be copied integrally with a hand-held digital video camera. The interference between the on-off/off-on transitions and the acquisition rate of most consumer-grade cameras creates disturbing visual artifacts. The system can even be adjusted to make sure a ‘VOID’ pattern appears in the recorded video. In this paper, we present how video frames are encoded and decoded and how the detection (and the nondetection) of temporal transitions allows for 3D reconstruction and copyright protection at the same time. Experimental results reveal that our system computes dense 3D maps at a rate of 11.25 fps and with an accuracy of approximately ±1/2 pixel, i.e. 56 microns when in focus and using a standoff distance of 75 cm. [ABSTRACT FROM AUTHOR]

In this paper, a patch based change detection method for polarimetric synthetic aperture radar (PolSAR) data is proposed. Because of the inherent speckle interference in SAR system, a despeckling procedure is important for change detection. However, the residual speckle still exists in the filtered data and is unevenly distributed, which is not considered in the available change detectors. Besides that, the detection methods with despeckling procedure cannot be adjusted based on the constant false alarm rate (CFAR). Specifically, the available PolSAR change detection methods are inefficient for the strong speckle interference cases due to the absence of the despeckling procedure. In order to overcome these drawbacks, an improved change detection framework for PolSAR data is proposed in this paper. The main innovations in the new framework include: (1) a despeckling procedure is added for PolSAR change detection, which makes the algorithm robust to strong noise; (2) a new equivalent number of looks (ENL) estimator is proposed for the PolSAR/SAR data filtered by non-local means filter; (3) the proposed method which detects the changes based on the filtered data can be adjusted with different CFARs. The experiments on both synthetic and real PolSAR datasets have demonstrated that the effectiveness of the proposed patch based PolSAR change detector (PPCD). [ABSTRACT FROM AUTHOR]

Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection. [ABSTRACT FROM AUTHOR]

Change detection of longitudinal fundus images is an important problem in computer aided diagnosis system (CAD). Detecting regions of change in multiple fundus images from the same eye is seldom developed in the literature due to the complication and interpretability. This paper presents a longitudinal change detection framework based on tensor robust principal component analysis (RPCA) for a long retinal fundus image serial. The proposed method chooses an image of the best condition in serial as the background, then models each image as a slice of tensor, utilizes total variation to constraint the temporal continuity of change regions, finally obtains the change regions by Tucker decomposition and alternating direction method of multipliers (ADMM). Comparing with the method based on matrix RPCA, tensor RPCA preserves the original spatial structure of each image, imposes the temporal continuity on change regions and models the background by patches to avoid the little disturbance of blood vessels. Results on a real fundus image serial are presented and show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Change vector analysis (CVA) is an effective and widely used unsupervised change detection algorithm in remote sensing. It separates changed pixels from unchanged pixels by binarizing bi-temporal difference image. However, the results and performance are affected by the image acquisitions at different dates and the threshold decision rules for change magnitudes, resulting in serious false and missed detections. This paper proposed a novel tri-temporal logic-verified change vector analysis (TLCVA) approach which can identify the errors of CVA through logical reasoning and judgement with an additional temporal image assistance. This approach can not only achieve a reliable modification to the original change detection results, but also produce two additional improved change detection results in the logical circulation of land surface change automatically. The proposed method consists of three parts: traditional CVA change detection, automated sample selection, and refined modification based on SVM posterior probability comparison in temporal space. It was experimented by land cover change detection from Sentinel-2 and Planet Labs images in three study areas located in Ma'anshan, Nanjing and Taizhou City. The results show that accuracies have significant improvements by the TLCVA approach, and omission and commission errors reduce obviously. The generalization, sensitivity and efficiency of the proposed approach were also analyzed in the experiments. It is concluded that different threshold decision methods of preliminary CVA in the proposed approach can work effectively and efficiently, and a small size of training samples selected from the automated sample decision method is enough to achieve improved change detection performance. [ABSTRACT FROM AUTHOR]

An unsupervised change detection method based upon wavelet fusion and Hybrid Kohonen FCM-σ is presented in this paper. Principle component analysis of the bi-temporal satellite images of same scene is computed for dimensionality reduction. The log ratio & absolute difference images of PC 1 are fused together using wavelet transform. Further HKFCM-σ classifier is used to classify the satellite image. Since the focus is on change detection thus the images are classified into two classes only representing change and unchanged areas. The simulation results of proposed method show that it can be effectively used for change detection. The result of the proposed technique is furthermore equalled with the existing change detection techniques available in the literature and it is observed that the proposed technique outperforms the other methods. [ABSTRACT FROM AUTHOR]

• Proposes an end-to-end network for biphasic image change detection. • Can better solve the problem of detection errors. • High degree of automation in the calculation process. • Enhanced feature extraction module was inroduced. Change detection is an effective means of monitoring surface changes. In order to solve the problem of reducing detection accuracy in remote sensing image change detection methods that only focus on extracting deep semantic information while ignoring high-resolution shallow information, this paper proposes an end-to-end multi-side output fusion deep supervised network for high-resolution biphasic remote sensing image change detection. The enhanced feature extraction module is used to increase the Receptive field and capture multi-scale features. The dense skip connection module utilizes skip connections to fuse shallow position information and deep semantic information, alleviating the problem of feature information loss. The deep supervision module with multiple side-outputs fusion utilizes the information complementarity between different scale feature maps, combined with multi-scale prediction maps, to improve the robustness of target scale changes. The combined Loss function is used to alleviate the problem of imbalance between positive and negative samples, so that more attention is paid to the learning of changing characteristics in the process of training the network. The proposed method was validated on CDD dataset and DSIFN dataset, with F1 scores of 91.58% and 86.69%, respectively, which were improved by 1.00% and 5.53% compared to the suboptimal network. [ABSTRACT FROM AUTHOR]

• A spatio-temporal Cokriging method is developed to sharpen land use data. • It is designed to aggregate classification codes rather than spectral values. • Spatio-temporal dependence is used to optimize LULC parameter characterization. • A high-resolution airborne imagery is used to sharpen time-series land use maps. • It generates both land use change probabilities and high-resolution land use maps. High quality land use/land cover (LULC) data with fine spatial resolution and frequent temporal coverage are indispensable for revealing detail information of the Earth's surface, characterizing LULC of the area, predicting the plausible land use changes, and assessing the viability and impacts of any development plans. While airborne imagery has high spatial resolution, it only provides limited temporal coverage over time. The LULC data from historical remote sensing images, such as those from Landsat, have frequent coverages over a long temporal period, but their spatial resolutions are low. This paper presents a spatio-temporal Cokriging method to sharpen LULC data and predict the trends of land use change. A set of time-series coarse resolution LULC maps and one frame of high spatial resolution airborne imagery of the Upper Mill Creek Watershed were used to illustrate the utility of our method. By explicitly describing the spatio-temporal dependence within and between different datasets, modelling the Anderson classification codes using spatial, temporal, and cross-covariance structures, and transforming the Anderson integer classification code to class probability, our method was able to resolve the differences between multi-source spatio-temporal LULC data, generate maps with sharpened and detailed land features, characterize the spatial and temporal LULC changes, reveal the trend of LULC change, and create a quality dataset invaluable for monitoring, assessing, and modelling LULC changes. [ABSTRACT FROM AUTHOR]

• Analysis of land cover changes of Darling Range. • Fourteen time series images between 1988 and 2014 were used. • Compares the performances of three machine learning algorithms. • The analysis outputs of random forest algorithm were validated. The Darling Range in Western Australia is a major bauxite producing region. Clearing, excavation and rehabilitation activities related to bauxite mining have influenced land cover within this region since mining commenced in the 1960s. This paper presents a study that used machine learning and time series visualisation to analyse the land cover changes of the Darling Range using time-lapse multispectral images, with the aim of understanding the impact of the mining activities and land rehabilitation patterns of the region. Land cover changes were analysed using 14 Landsat Thematic Mapper (TM) images between 1988 and 2014. The spatial distribution of land cover was classified automatically using machine learning algorithms, and their temporal changes were visualized for analysis. Supervised classification was carried out based on the six spectral features of the images using three machine learning algorithms, namely support vector machine (SVM), random forest (RF) and Naïve Bayes (NB). The results showed that the RF algorithm achieved overall accuracy, (ratio of correctly classified samples divided by the total number of samples), greater than 95% for all years. The temporal changes of land cover distribution over the study period were visualized using a change map. These changes were compared with land clearing and rehabilitation records of a mining company operating in that region. A close correlation was observed between the automated analysis outputs and the company's records. This work demonstrates the potential use of machine analysis in improving the accuracy of spatial data related to land clearing; and in monitoring vegetation recovery of closed and rehabilitated mines. [ABSTRACT FROM AUTHOR]

Multi-temporal analysis has been widely acknowledged as a promising method to derive soil moisture from radar backscatter observations. The method assumes that only soil moisture varies in the period of interest, while all other parameters such as vegetation water content and soil surface roughness are sufficiently time invariant. However, this assumption is not easy to satisfy in agricultural areas where cultivation practices such as ploughing and irrigation are irregularly conducted between radar acquisitions. The paper has proposed an unsupervised change detection method to serve as a pre-processing procedure for multi-temporal retrieval. Briefly, the temporal ratio of HV and the temporal difference of HV/VV and VV polarizations were selected as the optimal feature space, using a genetic algorithm based feature selection algorithm and an extensive synthetic data set. The change map is determined from a two-step procedure with the first step producing multiple over-detected change maps for the period of interest using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. The second step merges the multiple change maps to remove the false alarms with a principle similar to the ensemble machine learning. Evaluation on a synthetic data set demonstrated that the proposed method can largely remove the error in multi-temporal soil moisture retrieval that is caused by abrupt roughness and vegetation changes. Evaluation on real radar data sets, including airborne L-band radar, RADARSAT-2 at C-band and COSMO SkyMed at X-band, demonstrated an accurate identification (>0.9) while yielding a low false-alarm rate (<0.1). These results suggest that the method may be used as a pre-processing stage of global soil moisture retrieval from radar satellite missions with a high revisit frequency, such as Sentinel-1 and SAOCOM-1. • The HV, HV/VV & VV were selected for detecting slight roughness & vegetation changes. • Slight roughness & vegetation changes can be detected in an unsupervised manner. • Change detection information can improve the time series retrieval of soil moisture. [ABSTRACT FROM AUTHOR]

Change detection monitoring on multi-temporal remote sensed images is a persistent methodological challenge where the Dempster-Shafer, or evidence, Theory (DST) has been often applied. This paper presents a new method based on the use of DST for mining bi-temporal remotely sensed images change. The main idea is based on the investigation, analysis and interpretation of different types of conflict between two bi-temporal mass distributions. The reasoning process is focused on the conflict significance and its "partial" causes. In fact, the global conflict that occurs during the joint exploitation of multi-temporal images gives general and non-sufficiently concise information. However, the partial conflict provides rich and important information with regards to the disagreement between knowledge sources. For computing the partial conflict between focal elements, the geometric representation of mass distributions is exploited. The obtained conflict measures, caused by change, are analyzed latter by a new algorithm for drifting binary change map and identifying change directions. The effectiveness and reliability of the proposed approach are shown through experimentations on simulated changed images as well as using multi-temporal Landsat satellite images where qualitative criteria as well as quantitative measures are applied. The performances of the proposed approach, in terms of changed area recognition, are compared to three different and widely used conflict measures: the Empty-set mass, the Jousselme's distance and the Cosine measure. It is shown that the developed change detection approach outperforms these conflict measures. [ABSTRACT FROM AUTHOR]