Hyperspectral image (HSI) change detection is a technique that can identify the changes occurring between the bitemporal HSIs covering the same geographic area. The field of change detection has witnessed the proposal and successful implementation of numerous methods. However, a majority of these approaches adhere to the centralized learning paradigm, which requires data transmission to a central server for training. The sensitivity of remote sensing data generally prohibit their sharing across different clients. Furthermore, manual labeling is a costly effort in practically. In this paper, we propose a spatial-spectral-temporal collaborative Mamba-based active federated hyperspectral change detection (MambaFedCD) framework, which utilizes the limited labeled samples from multiple clients to achieve change detection while ensuring the data privacy of each client. Specifically, there are three key characteristics: 1) a spatial-spectral-temporal collaborative Mamba-based change detection ( ${{\text {S}}^{2}}{\text {TMamba}}$ ) model is proposed to efficiently synergize the temporal and global spatial-spectral information of the bitemporal HSIs for change detection; 2) a difference feature diversity correction-based model aggregation (DFDCMA) strategy is devised to incorporate the diversity of difference features for rational allocation of weight factors among clients and to facilitate effective aggregation of the global model; 3) we propose a multi-decision federated active learning (MDFAL) strategy that selects both error-prone and valuable samples for model training to alleviate the burden of sample labeling. Comprehensive experiments conducted on commonly utilized datasets demonstrate that the proposed method outperforms other state-of-the-art methods. The code is available at https://github.com/Jiahuiqu/MambaFedCD.

Change detection aims to identify land cover changes by analyzing multitemporal images that cover the same area. However, It may be difficult to effectively obtain high-quality multitemporal images with the same modality in real dynamic scenarios. The rapid development of remote sensing technology enables collaborative observation of multimodal images, but it is challenging for uni-modal image-specific methods to overcome modal discrepancy and achieve complementary advantage detection. To this end, we propose a bidirectional diffusion guided collaborative change detection model (Bi-DiffCD) for arbitrary-modal images, which eliminates the modal discrepancy between arbitrary-modal images through the bidirectional diffusion and makes full use of the multilevel complementary advantage features to improve the detection accuracy. Specifically, a conditional diffusion-based bidirectional modal alignment module (CDBMA) is designed to step-wise align the modal attribute bidirectionally while preserving the multimodal complementary features. Furthermore, a multilevel complementary feature collaborative change detection module (MLCCD) is proposed to collaborate the multilevel enhanced complementary change information from transformed images and potential features for change detection. Experiments have been conducted on three widely used and one self-made multimodal datasets to demonstrate the effectiveness of the proposed method with different combinations of modalities. Code is available at https://github.com/Jiahuiqu/Bi-DiffCD.

Hyperspectral image super-resolution (HISR) is a technique that can break through the limitation of imaging mechanism to obtain the hyperspectral image (HSI) with high spatial resolution. Although some progress has been achieved by existing methods, most of them directly learn the spatial-spectral joint mapping between the observed images and the target high-resolution HSI (HrHSI), failing to fully reserve the spectral distribution of low-resolution HSI (LrHSI) and the spatial distribution of high-resolution multispectral imagery (HrMSI). To this end, we propose a spatial-spectral-bilateral cycle-diffusion framework (S2CycleDiff) for HISR, which can step-wise generate the HrHSI with high spatial-spectral fidelity by learning the conditional distribution of spatial and spectral super-resolution processes bilaterally. Specifically, a customized conditional cycle-diffusion framework is designed as the backbone to achieve the spatial-spectral-bilateral super-resolution by repeated refinement, wherein the spatial/spectral guided pyramid denoising (SGPD) module seperately takes HrMSI and LrHSI as the guiding factors to achieve the spatial details injection and spectral correction. The outputs of the conditional cycle-diffusion framework are fed into a complementary fusion block to integrate the spatial and spectral details to generate the desired HrHSI. Experiments have been conducted on three widely used datasets to demonstrate the superiority of the proposed method over state-of-the-art HISR methods. The code is available at https://github.com/Jiahuiqu/S2CycleDiff.

Recently, deep neural networks (DNNs) have been widely used in hyperspectral image change detection (HSI-CD). Generally, training such a DNN-based HSI-CD network often requires a large number of labeled training samples. However, it is time-consuming, labor-intensive, or even infeasible to label training samples in practice. In this article, we propose a feature mutual representation-based graph domain adaptive network (FGDANet) for unsupervised HSI-CD. This method constructs a pseudosiamese backbone consisting of two customized unsupervised learning domains, which can make full use of the information from different domains through the graph domain adaptation strategy to improve the feature expression capability and generalization. There are three key characteristics: first, in each customized unsupervised learning domain, a graph convolutional network (GCN)-based difference feature extraction architecture is designed to model the local and global dependence among the features of multitemporal HSIs; second, a progressive graph-to-pixel joint constraint strategy (PJCS) is proposed to provide the high-confidence training sample labels for the unsupervised learning of the network in each domain; and third, the homogeneous mutual representation joint graph feature alignment (HJGFA) module of the graph domain adaptation strategy can make full use of the difference features from the two domains through the information interaction to facilitate the model to capture the changed and unchanged essential characteristics. The experimental results on four HSI datasets demonstrate the superiority of the proposed FGDANet. Code is available athttps://github.com/Jiahuiqu/FGDANet.

Hyperspectral image (HSI) change detection is a technique to observe the change information between the multitemporal HSIs, which is currently considered a major focus of research in the filed of remote sensing intelligent interpretation. Most existing deep learning-based methods have created satisfactory performance, but these methods lack transparency and have poor generalization. To tackle the problems outlined above, we propose a dictionary learning-guided deep interpretable network for hyperspectral change detection, which unfolds a dictionary learning-based change detection model into an interpretable deep neural network. Specifically, we first design a dictionary learning-based change detection model, whose solution process can be decomposed into two iterative subproblems. Then, the mathematical model can be unfolded into a dual-branch deep neural network with two modules iterating with each other. Finally, the difference map of the coefficients output from the ultimate stage is classified to obtain the change detection result. Experimental results prove that the proposed method has comparable or even better performance than state-of-the-art methods.

Hyperspectral image (HSI) change detection is a technique for detecting the changes between the multitemporal HSIs of the same scene. Many existing change detection methods have achieved good results, but there still exist problems as follows: 1) mixed pixels exist in HSI due to the low spatial resolution of hyperspectral sensor and other external interference and 2) many existing deep learning-based networks cannot make full use of the correlation difference information between the bitemporal images. These problems are not conducive to further improving the accuracy of change detection. In this article, we propose an abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention (AMCAN-HMSchA) for HSI change detection, which hierarchically highlights the correlation difference information at the subpixel level to detect the subtle changes. The endmember sharing-based abundance matrix learning module (AMLM) maps the changed information between bitemporal HSIs to the corresponding abundance matrices. The hierarchical MSchA extracts the enhanced difference features by constantly comparing the self-correlation with cross correlation between the abundance matrices of the HSIs. Then, the difference features are concatenated and fed into the fully connected layers to obtain the change map. Experiments on three widely used datasets show that the proposed method has superior performance compared with other state-of-the-art methods.