An Attention-Based Multiscale Spectral-Spatial Network for Hyperspectral Target Detection

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.