Deep learning methods have been extensively applied to hyperspectral (HS) image change detection (CD) tasks and achieved promising performance. However, the beneficial joint spatial–spectral–temporal information provided by the HS images has not been fully used. Since the bi-temporal HS images are highly symmetric, in this letter, we propose a novel cross-temporal interaction symmetric attention network (CSANet), which can effectively extract and integrate the joint spatial–spectral–temporal features of the HS images, at the same time to enhance feature discrimination ability of the changes. Specifically, we propose a novel cross-temporal interaction symmetric attention (CSA) module to interact with the bi-temporal HS information, where self-attention is combined to enhance the feature representation ability of each temporal image, and the cross-temporal attention is utilized to integrate the difference features oriented from each temporal feature embedding. On this basis, we design a Siamese network structure equipped with the CSA to hierarchically extract the change information in a symmetric pattern. Experimental results on three public HS image CD datasets show that the proposed CSANet CD framework achieves a significant improvement when compared with the state-of-the-art (SOTA) methods. The source code of the proposed framework will be released at https://github.com/srxlnnu .

Convolutional neural networks (CNNs) have been extensively applied to hyperspectral (HS) image classification tasks and achieved promising performance. However, for CNN-based HS image classification methods, it is hard to depict the dependencies among HS image pixels in long-range distanced positions and bands. Moreover, the limited receptive field of the convolutional layers extremely hinders the development of the CNN structure. To tackle these problems, in this article, the novel bottleneck spatial–spectral transformer (BS2T) is proposed to depict the long-range global dependencies of HS image pixels, which can be regarded as a feature extraction module for HS image classification networks. More specifically, inspired by bottleneck transformer in computer vision, for HS image feature extraction, the proposed BS2T is incorporated with a feature contraction module, a multihead spatial–spectral self-attention (MHS2A) module, and a feature expansion module. In this way, convolutional operations are replaced by the MHS2A to capture the long-range dependency of HS pixels regardless of their spatial position and distance. Meanwhile, in the MHS2A module, to highlight the spectral features of HS images, we introduce the spectral information and content spatial positional information to classical multihead self-attention to make the attention more positional aware and spectral aware. On this basis, a dual-branch HS image classification framework based on 3-D CNN and BS2T is defined for jointly extracting the local–global features of HS images. Experimental results on three public HS image classification datasets show that the proposed classification framework achieves a significant improvement when compared with the state-of-the-art methods. The source code of the proposed framework can be downloaded from https://github.com/srxlnnu/BS2T .

The acquisition of multisource remote-sensing (RS) data has become more and more convenient due to the boom and innovation of RS imaging technology. The fusion and classification of hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) data has become a research hotspot because of their excellent complementarity and the vigorous development of deep learning (DL) provides effective methods. Most of the existing methods based on convolution neural networks (CNNs) have fixed convolution kernels, making it difficult to extract multiscale detailed features. In this letter, we propose a multiscale pyramid fusion framework based on spatial–spectral cross-modal attention (S2CA) for HSIs and LiDAR classification, which has strong multiscale information learning ability, especially in areas with complex information changes, thereby improving classification accuracy. Multiscale pyramid convolution is used to extract multiscale features, and an effective feature recalibration (EFR) module is used to enhance features and suppress useless information at each scale. To increase the interactivity of information between modes, we propose an S2CA module, which uses the features of different modes to enhance each other. Three real public datasets are used for the experiment. Compared with the existing advanced methods, the proposed method achieves the best results. The source code of the multiscale S2CA network (MS2CANet) will be public at https://github.com/junhengzhu/MS2CANet .