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Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper's capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM's capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data. [ABSTRACT FROM AUTHOR]

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR's performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has recently achieved outstanding performance in change detection of multitemporal images. However, most existing DL-based change detection methods still suffer from the problem of insufficient labeled training samples. To overcome this limitation, an unsupervised superpixel-guided self-supervised learning network (S3Net) is proposed for detecting changes occurred on the land surface. By performing principal component analysis on two input images, a triple-channel pseudocolor image containing the main information of both the images is first generated, which is used for superpixel segmentation to produce homogeneous image objects. Then, a Siamese network composing of two identical subnetworks with shared weight based on transfer learning is trained for pretext task in a self-supervised learning way, aiming to obtain multiscale object-level spatial feature difference images. On this basis, a high-quality difference image is generated by incorporating the pixel-level and object-level difference information using a simple weighted fusion strategy, which can be analyzed by thresholding to produce the final binary change map. The experimental results on four real-world datasets from different sensors show that the proposed approach can obtain superior performance in comparison to several state-of-the-art change detection methods, which further demonstrates its effectiveness and practicability. We make our data and code publicly available (https://github.com/OMEGA-RS/S3Net_CD). [ABSTRACT FROM AUTHOR]

Landslide inventory mapping (LIM) on the basis of change detection techniques has potential significance for landslide disaster analysis. In this letter, a novel LIM approach based on the adaptive histogram-mean distance (AHMD) is proposed, which adaptively considers spatial contextual information of different landslide regions to improve the detection performance. First, to adapt the shape, size, and distribution of various landslides, an adaptive region around a pixel is extracted by a novel adaptive region extension algorithm without parameter setting. Second, the pixels within the adaptive region are taken to construct the spectral frequency histograms, and then, the adaptive histogram mean (AHM) is developed as the feature of a histogram. Third, the AHMD is defined based on the bin-to-bin (B2B) distance to measure change magnitude between the pairwise AHMs. Finally, LIM can be obtained by a supervised threshold method called double-window flexible pace search (DFPS). Experimental results tested on two real datasets with a very high spatial resolution (VHR) demonstrate the outperformance of the proposed AHMD approach with seven comparative methods. [ABSTRACT FROM AUTHOR]

Unsupervised change detection based on supervised or semisupervised classifiers has achieved strong adaptability and robustness to obtain satisfactory change detection results. However, these methods suffer from an issue that it is hard to collect reliable training samples in an unsupervised manner. In this article, a group self-paced learning (GSPL) framework is proposed to mine the reliable training samples. In the proposed method, each sample is assigned a weight to indicate its reliability. The proposed scheme is able to learn the weighted samples and update the weights iteratively in a self-paced manner to identify the reliable training samples. In the phase of updating weights, a grouping strategy is designed to avoid selecting training samples from homogeneous regions. Furthermore, a novel time-varying self-paced regularizer is proposed to automatically determine the learning scheme of self-paced learning. Finally, three classifiers, including SoftMax, backpropagation neural network, and support vector machine, are investigated under this proposed framework. Experiments on five change detection data sets demonstrate that the proposed framework can significantly outperform those state-of-art methods for change detection in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Due to the distinct statistical properties in cross-sensor images, change detection in heterogeneous images is much more challenging than in homogeneous images. In this letter, we adopt a conditional generative adversarial network (cGAN) to transform the heterogeneous synthetic aperture radar (SAR) and optical images into some space where their information has a more consistent representation, making the direct comparison feasible. Our proposed framework contains a cGAN-based translation network that aims to translate the optical image with the SAR image as a target, and an approximation network that approximates the SAR image to the translated one by reducing their pixelwise difference. The two networks are updated alternately and when they are both trained well, the two translated and approximated images can be considered as homogeneous, from which the final change map can be acquired by direct comparison. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

Multispectral image change detection based on deep learning generally needs a large amount of training data. However, it is difficult and expensive to mark a large amount of labeled data. To deal with this problem, we propose a generative discriminatory classified network (GDCN) for multispectral image change detection, in which labeled data, unlabeled data, and new fake data generated by generative adversarial networks are used. The GDCN consists of a discriminatory classified network (DCN) and a generator. The DCN divides the input data into changed class, unchanged class, and extra class, i.e., fake class. The generator recovers the real data from input noises to provide additional training samples so as to boost the performance of the DCN. Finally, the bitemporal multispectral images are input to the DCN to get the final change map. Experimental results on the real multispectral imagery datasets demonstrate that the proposed GDCN trained by unlabeled data and a small amount of labeled data can achieve competitive performance compared with existing methods. [ABSTRACT FROM AUTHOR]

Abstract Owing to the rapid development of remote sensing technology, various types of data can be easily acquired at present. However, it has become an important but more challenging task for effectively highlighting changes occurring on the land surface from these available data. In this paper, we propose an iterative feature mapping network learning framework for identifying multiple changes with focus on multi-source images, which are often obtained from sensors with different imaging modalities. Firstly, high-level and robust feature representations are extracted from multi-source images via unsupervised feature learning. Then, on this basis, an iterative feature mapping network is established to transform these features into a common high-dimensional feature space. It aims to learn more discriminative features by shrinking the difference between the paired features of unchanged positions while enlarging that of changed ones. Note that the network parameters are learned by optimizing a well-designed objective function, and the whole learning process is fully unsupervised. Finally, based on a hierarchical tree for clustering analysis, all possible change classes can be detected accurately. In addition, the proposed framework is found to be also suitable for change detection in homogeneous images. The impressive experimental results obtained over different types of remote sensing images demonstrate the effectiveness and robustness of the proposed model. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing technology, how to accurately detect changes that have occurred on the land surface has been a critical task, particularly when images come from different satellite sensors. In this letter, we propose an unsupervised change detection method for heterogeneous synthetic aperture radar (SAR) and optical images based on the logarithmic transformation feature learning framework. First, the logarithmic transformation is applied to the SAR image that aims to achieve similar statistical distribution properties as the optical image. Then, high-level feature representations can be learned from the transformed image pair via joint feature extraction, which are used to select reliable samples for training a neural network classifier. When it is trained well, a robust change map can be obtained, thus identifying changed regions accurately. The experimental results on three real heterogeneous data sets demonstrate the effectiveness and superiority of the proposed method compared with other existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Change detection can be treated as a generative learning procedure, in which the connection between bitemporal images and the desired change map can be modeled as a generative one. In this letter, we propose an unsupervised change detection method based on generative adversarial networks (GANs), which has the ability of recovering the training data distribution from noise input. Here, the joint distribution of the two images to be detected is taken as input and an initial difference image (DI), generated by traditional change detection method such as change vector analysis, is used to provide prior knowledge for sampling the training data based on Bayesian theorem and GAN’s min–max game theory. Through the continuous adversarial learning, the shared mapping function between the training data and their corresponding image patches can be built in GAN’s generator, from which a better DI can be generated. Finally, an unsupervised clustering algorithm is used to analyze the better DI to obtain the desired binary change map. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper mainly introduces a novel deep learning and mapping (DLM) framework oriented to the ternary change detection task for information unbalanced images. Different from the traditional intensity-based methods available, the DLM framework is based on the operation of the features extracted from the two images. Due to the excellent performance of deep learning in information representation and feature learning, two networks are used here. First, the stacked denoising autoencoder is used on two images, serving as a feature extractor. Then after a sample selection process, the stacked mapping network is employed to obtain the mapping functions, establishing the relationship between the features for each class. Finally, a comparison between the features is made and the final ternary map is generated through the clustering of the comparison result. This work is highlighted by two aspects. Firstly, previous works focus on two images with similar properties, whereas the DLM framework is based on two images with quite different properties, which is a usually encountered case. Secondly, the DLM framework is based on the analysis of feature instead of superficial intensity, which avoids the corruptions of unbalanced information to a large extent. Parameter tests on three datasets provide us with the appropriate parameter settings and the corresponding experimental results demonstrate its robustness and effectiveness in terms of accuracy and time complexity. [ABSTRACT FROM AUTHOR]

With the rapid technological development of various satellite sensors, high-resolution remotely sensed imagery has been an important source of data for change detection in land cover transition. However, it is still a challenging problem to effectively exploit the available spectral information to highlight changes. In this paper, we present a novel change detection framework for high-resolution remote sensing images, which incorporates superpixel-based change feature extraction and hierarchical difference representation learning by neural networks. First, highly homogenous and compact image superpixels are generated using superpixel segmentation, which makes these image blocks adhere well to image boundaries. Second, the change features are extracted to represent the difference information using spectrum, texture, and spatial features between the corresponding superpixels. Third, motivated by the fact that deep neural network has the ability to learn from data sets that have few labeled data, we use it to learn the semantic difference between the changed and unchanged pixels. The labeled data can be selected from the bitemporal multispectral images via a preclassification map generated in advance. And then, a neural network is built to learn the difference and classify the uncertain samples into changed or unchanged ones. Finally, a robust and high-contrast change detection result can be obtained from the network. The experimental results on the real data sets demonstrate its effectiveness, feasibility, and superiority of the proposed technique. [ABSTRACT FROM PUBLISHER]

With the increase of multisource data available from remote sensing platforms, it is demanding to develop unsupervised techniques for change detection from multisource data. The difference in imaging mechanism makes it difficult to carry out a direct comparison between multisource data in original observation spaces. Different sensors provide different descriptions on the same truth in low-dimension observation spaces, but the same truth indicates the comparability of multisource data in some high-dimensional feature spaces. Inspired by this, we try to solve this problem by transforming multisource data into a common high-dimension feature space. In this paper, an iterative coupled dictionary learning (CDL) model is proposed for multisource image change detection. This model aims to establish a pair of coupled dictionaries, one of which is responsible for the data from one sensor, whereas the other is responsible for the data from another sensor. The atoms from these two coupled dictionaries have a one-to-one correspondence at the same location. Such a property guarantees the transferability of the reconstruction coefficients between bitemporal patch pairs and provides us a desired mechanism to bridge multisource data and highlight changes. The contributions can be summarized as follows: CDL is designed to explore the intrinsic difference of multisource data for change detection in a high-dimension feature space, and an iterative scheme for unsupervised sample selection is proposed to keep the purity of training samples and gradually optimize the current coupled dictionaries. The experimental results have demonstrated the feasibility, effectiveness, and robustness of the proposed framework. [ABSTRACT FROM PUBLISHER]

Due to the noise interference and redundancy in multispectral images, it is promising to transform the available spectral channels into a suitable feature space for relieving noise and reducing the redundancy. The booming of deep learning provides a flexible tool to learn abstract and invariant features directly from the data in their raw forms. In this letter, we propose an unsupervised change detection technique for multispectral images, in which we combine deep belief networks (DBNs) and feature change analysis to highlight changes. First, a DBN is established to capture the key information for discrimination and suppress the irrelevant variations. Second, we map bitemporal change feature into a 2-D polar domain to characterize the change information. Finally, an unsupervised clustering algorithm is adopted to distinguish the changed and unchanged pixels, and then, the changed types can be identified by classifying the changed pixels into several classes according to the directions of feature changes. The experimental results demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM PUBLISHER]

Multi-spatial-resolution change detection is a newly proposed issue and it is of great significance in remote sensing, environmental and land use monitoring, etc. Though multi-spatial-resolution image-pair are two kinds of representations of the same reality, they are often incommensurable superficially due to their different modalities and properties. In this paper, we present a novel multi-spatial-resolution change detection framework, which incorporates deep-architecture-based unsupervised feature learning and mapping-based feature change analysis. Firstly, we transform multi-resolution image-pair into the same pixel-resolution through co-registration, followed by details recovery, which is designed to remedy the spatial details lost in the registration. Secondly, the denoising autoencoder is stacked to learn local and high-level representation/feature from the local neighborhood of the given pixel, in an unsupervised fashion. Thirdly, motivated by the fact that multi-resolution image-pair share the same reality in the unchanged regions, we try to explore the inner relationships between them by building a mapping neural network. And it can be used to learn a mapping function based on the most-unlikely-changed feature-pairs, which are selected from all the feature-pairs via a coarse initial change map generated in advance. The learned mapping function can bridge the different representations and highlight changes. Finally, we can build a robust and contractive change map through feature similarity analysis, and the change detection result is obtained through the segmentation of the final change map. Experiments are carried out on four real datasets, and the results confirmed the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an effective classification approach for hyperspectral image, based upon a novel discontinuity adaptive class-relative nonlocal means (DACNLM) algorithm and embedding it in the global energy function by energy fusion strategy. Inspired from recent works related to nonlocal means, we extend this framework to label space, assuming that nonlocal similar patches have similar label structures. Thus, similar local structures and nonlocal averaging process are combined by the proposed DACNLM algorithm. The Shannon entropy is adopted to define the distribution of energy. The energy function is then improved by fusion strategy that selects the energy corresponding to the lowest uncertainty. As a sequence, the hyperspectral image classification task stated in term of energy minimization is efficiently solved by graph cuts algorithm. Experiments on two real hyperspectral data sets are provided to demonstrate the effectiveness of our hyperspectral classification algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel change detection approach is proposed for multitemporal synthetic aperture radar (SAR) images. The approach is based on two difference images, which are constructed through intensity and texture information, respectively. In the extraction of the texture differences, robust principal component analysis technique is used to separate irrelevant and noisy elements from Gabor responses. Then graph cuts are improved by a novel energy function based on multivariate generalized Gaussian model for more accurately fitting. The effectiveness of the proposed method is proved by the experiment results obtained on several real SAR images data sets. [Copyright &y& Elsevier]

Change detection is now routinely applied in many application domains, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of algorithms rise, the demand for processing power is increasing. In this paper, we propose PLog-FLICM , a parallel algorithm for change detection, which includes two steps: (1) generate the difference image based on the log-ratio operator; (2) detect changes in the difference image by using a modified fuzzy c-means clustering algorithm. PLog-FLICM is implemented on AMD Accelerated Parallel Processing SDK based on Open Computing Language. The parallel characteristics and implementation details of the proposed PLog-FLICM algorithm are presented. Experiments on several Synthetic Aperture Radar images demonstrate that the proposed algorithm outperforms other algorithms, and the designed parallel algorithm can greatly reduce the computational time of the change detection algorithm. Furthermore, we investigate the performance portability of PLog-FLICM in the different central processing unit and graphics processing unit platforms. Experimental results show that they have also achieved good parallel performance. [ABSTRACT FROM AUTHOR]

This letter presents a novel neighborhood-based ratio (NR) operator to produce a difference image for change detection in synthetic aperture radar (SAR) images. In order to reduce the negative influence of speckle noise on SAR images, the proposed NR operator produces a difference image by combining gray level information and spatial information of neighbor pixels. The performance comparisons of the proposed operator with a traditional ratio operator and a log-ratio operator indicate that the NR operator is superior to these traditional methods and produces better detection results. [ABSTRACT FROM PUBLISHER]

Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based on generative adversarial networks (GANs). The generator captures the distribution of the bitemporal multi-spectral image data and transforms it into change detection results, and these change detection results (as the fake data) are input into the discriminator to train the discriminator. The results obtained by pre-classification are also input into the discriminator as the real data. The adversarial training can facilitate the generator learning the transformation from a bitemporal image to a change map. When the generator is trained well, the generator has the ability to generate the final result. The bitemporal multi-spectral images are input into the generator, and then the final change detection results are obtained from the generator. The proposed method is completely unsupervised, and we only need to input the preprocessed data that were obtained from the pre-classification and training sample selection. Through adversarial training, the generator can better learn the relationship between the bitemporal multi-spectral image data and the corresponding labels. Finally, the well-trained generator can be applied to process the raw bitemporal multi-spectral images to obtain the final change map (CM). The effectiveness and robustness of the proposed method were verified by the experimental results on the real high-resolution multi-spectral image data sets. [ABSTRACT FROM AUTHOR]

Due to the fact that weak and fake laser-induced damages may occur in the surface of optical elements in high-energy laser facilities, it is still a challenging issue to effectively detect the real laser-induced damage changes of optical elements in optical images. Different from the traditional methods, in this paper, we put forward a similarity metric optimization driven supervised learning model to perform the laser-induced damage change detection task. In the proposed model, an end-to-end siamese convolutional neural network is designed and trained which can integrate the difference image generating and difference image analysis into a whole network. Thus, the damage changes can be highlighted by the pre-trained siamese network that classifies the central pixel between input multi-temporal image patches into changed and unchanged classes. To address the problem of unbalanced distribution between positive and negative samples, a modified average frequency balancing based weighted softmax loss is used to train the proposed network. Experiments conducted on two real datasets demonstrate the effectiveness and superiority of the proposed model. • This article presents a novel model for damage change detection of optical elements. • A similarity metric optimization driven deep learning model is proposed. • An end-to-end siamese CNN is designed for the damage change detection task. • A modified weighted softmax loss is utilized to train the network. [ABSTRACT FROM AUTHOR]

• We propose a new deep learning-based CD method, S2PNet, to combat the difficulties brought by some unique features of VHR remote sensing images. More precisely, the challenges of inadequate pattern separability and high land cover diversity are further overcome by our method when dealing with CD tasks. • We propose a novel feature pyramid module, SFP, to cope with multiscale change objects through the integration of the features at different layers with different spatial sizes. Compared to other PP-based modules, our SFP can acquire more authentic location information of multi-scale objects in VHR images. • We propose a dual-dimensional attention mechanism, PSA. It has two branches which are developed to refine feature maps in different dimensions, i.e., spatial-wise and channel-wise branches. Different with conventional similar mechanisms, the two branches of PSA run fully parallel, which will eliminate the interference with each other. • Comprehensive experiments have been conducted over several challenging public large-scale VHR change detection data sets. And corresponding experimental results indicate that the proposed S2PNet is able to outperform other state-of-the-art CD methods. Land cover change detection (CD) in very-high-resolution (VHR) images is still impeded by weak pattern separability and land cover complexity. To address these challenges, a self-structured pyramid network (S 2 PNet) with a parallel spatial-channel attention mechanism (PSAM) and a self-structured feature pyramid (SFP) is proposed for a finer annotation of changed land cover. The proposed PSAM refines the features of different levels in dual-branch coordinated by running parallel without mutual influence for a better recognition of varied objects, which can lead to less incorrectly detected land cover. And the SFP integrates the embedded multi-scale features to acquire an improved cognition over multi-scale objects, which can contribute to a more complete annotation over diverse objects. All-round experiments over several widely used open large-scale VHR CD data sets are carried out, which indicate the efficiency and effectiveness of the proposed method. Related comparisons suggest that the proposed method can achieve higher performance over several existing state-of-the-art CD methods. The source codes will be released at https://github.com/HaiXing-1998/S2PNet-CD. [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]

• 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]

Compared with optical sensors, the all-weather and day-and-night imaging ability of Synthetic Aperture Radar (SAR) makes it competitive for burnt area mapping. This study investigates the potential of Sentinel-1 C-band SAR sensors in burnt area mapping with an implicit Radar Convolutional Burn Index (RCBI). Based on multitemporal Sentinel-1 SAR data, a convolutional networks-based classification framework is proposed to learn the RCBI for highlighting the burnt areas. We explore the mapping accuracy level that can be achieved using SAR intensity and phase information for both VV and VH polarizations. Moreover, we investigate the decorrelation of Interferometric SAR (InSAR) coherence to wildfire events using different temporal baselines. The experimental results on two recent fire events, Thomas Fire (Dec., 2017) and Carr Fire (July, 2018) in California, demonstrate that the learnt RCBI has a better potential than the classical log-ratio operator in highlighting burnt areas. By exploiting both VV and VH information, the developed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the Thomas Fire and Carr Fire, respectively. [ABSTRACT FROM AUTHOR]