Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary Change Detection (BCD) since it enables detailed change analysis in the observed areas. Previous works established triple-branch Convolutional Neural Network (CNN) architectures as the paradigm for SCD. However, it remains challenging to exploit semantic information with a limited amount of change samples. In this work, we investigate to jointly consider the spatio-temporal dependencies to improve the accuracy of SCD. First, we propose a Semantic Change Transformer (SCanFormer) to explicitly model the ’from-to’ semantic transitions between the bi-temporal RSIs. Then, we introduce a semantic learning scheme to leverage the spatio-temporal constraints, which are coherent to the SCD task, to guide the learning of semantic changes. The resulting network (SCanNet) significantly outperforms the baseline method in terms of both detection of critical semantic changes and semantic consistency in the obtained bi-temporal results. It achieves the SOTA accuracy on two benchmark datasets for the SCD.

Hyperspectral (HS) and multispectral (MS) image fusion aims at producing high-resolution HS (HRHS) images. However, the existing methods could not simultaneously consider the structures in both the spatial and spectral domains of the HS cube. In order to effectively preserve spatial-spectral structures in HRHS images, we propose a new low-resolution HS (LRHS) and high-resolution MS (HRMS) image fusion method based on spatial-spectral-graph-regularized low-rank tensor decomposition (SSGLRTD) in this paper. First, we reformulate the image fusion problem as a low-rank tensor decomposition model to utilize the low-rank property in the HS image. Then, two graphs are constructed in spatial and spectral domains, respectively. One of them is derived from the HRMS image for the spatial consistency, and the other is inferred from the LRHS image for spectral smoothness. Finally, the SSGLRTD fusion framework is established by combining all regularizers. With these two graphs, the spatial correlation and the spectral structure in the fused HRHS images are efficiently preserved. The experimental results on different datasets reveal that the proposed fusion method outperforms several existing fusion methods in terms of visual analysis and numerical comparison.

Thanks to their capability of modeling global information, transformers have been recently applied to change detection in remote sensing images. Generally, the changes in terms of shape and appearance of objects lead to relation changes among these objects in multi-temporal images. However, in this context, the attention mechanism in transformers has not been fully explored yet to learn relation changes in the observed scenes. In this paper, we analyze the relation changes in multi-temporal images and propose a cross-temporal difference (CTD) attention to capture these changes efficiently. Through the CTD attention, the changed areas are distinguished better from the unchanged areas. Based on the CTD attention, two CTD-transformer encoders are constructed to extract the features of changed areas from the embedded tokens of multi-temporal images in a cross manner. Then, the extracted features at the coarse scale are further improved to the fine-scale by the corresponding CTD-transformer decoders. In addition, consistency-perception blocks (CPBs) are designed to preserve the structures and contours of changed areas. Finally, all extracted features from multi-temporal images are concatenated to produce the desired change map. Compared to state-of-the-art methods, experimental results on LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that the proposed method produces better performance. The source code is available at https://github.com/RSMagneto/CTD-Former.

In this article, we present a new pansharpening method, a zero-reference generative adversarial network (ZeRGAN), which fuses low spatial resolution multispectral (LR MS) and high spatial resolution panchromatic (PAN) images. In the proposed method, zero-reference indicates that it does not require paired reduced-scale images or unpaired full-scale images for training. To obtain accurate fusion results, we establish an adversarial game between a set of multiscale generators and their corresponding discriminators. Through multiscale generators, the fused high spatial resolution MS (HR MS) images are progressively produced from LR MS and PAN images, while the discriminators aim to distinguish the differences of spatial information between the HR MS images and the PAN images. In other words, the HR MS images are generated from LR MS and PAN images after the optimization of ZeRGAN. Furthermore, we construct a nonreference loss function, including an adversarial loss, spatial and spectral reconstruction losses, a spatial enhancement loss, and an average constancy loss. Through the minimization of the total loss, the spatial details in the HR MS images can be enhanced efficiently. Extensive experiments are implemented on datasets acquired by different satellites. The results demonstrate that the effectiveness of the proposed method compared with the state-of-the-art methods. The source code is publicly available at https://github.com/RSMagneto/ZeRGAN .

Finding accurate injection components is the key issue in pan-sharpening methods. In this paper, a low-rank pan-sharpening (LRP) model is developed from a new perspective of offset learning. Two offsets are defined to represent the spatial and spectral differences between low-resolution multispectral and high-resolution multispectral (HRMS) images, respectively. In order to reduce spatial and spectral distortions, spatial equalization and spectral proportion constraints are designed and cast on the offsets, to develop a spatial and spectral constrained stable low-rank decomposition algorithm via augmented Lagrange multiplier. By fine modeling and heuristic learning, our method can simultaneously reduce spatial and spectral distortions in the fused HRMS images. Moreover, our method can efficiently deal with noises and outliers in source images, for exploring low-rank and sparse characteristics of data. Extensive experiments are taken on several image data sets, and the results demonstrate the efficiency of the proposed LRP.