LAUSR

Deep-learning-based methods have made great progress in hyperspectral target detection (HTD). Unfortunately, the insufficient utilization of spatial information in most methods leaves deep-learning-based methods to confront ineffectiveness. To ameliorate this issue, an attention-based multiscale spectral–spatial detector (AMSSD) for HTD is proposed. First, the AMSSD leverages the Siamese structure to establish a similarity discrimination network, which can enlarge intraclass similarity and interclass dissimilarity to facilitate better discrimination between the target and the background. Second, 1-D convolutional neural network (CNN) and vision Transformer (ViT) are used combinedly to extract spectral–spatial features more feasibly and adaptively. The joint use of spectral–spatial information can obtain more comprehensive features, which promotes subsequent similarity measurement. Finally, a multiscale spectral–spatial difference feature fusion module is devised to integrate spectral–spatial difference features of different scales to obtain more distinguishable representation and boost detection competence. Experiments conducted on two HSI datasets indicate that the AMSSD outperforms seven compared methods.