Triplet Spectralwise Transformer Network for Hyperspectral Target Detection

Recently, deep learning methods have demonstrated their potentials in extracting spectral information for hyperspectral images and have been widely applied in hyperspectral target detection (HTD). However, prior deep learning methods, represented by the convolutional neural networks, mainly focus on the local information and representation, which cannot well capture the long-range dependence. Besides, limited target references cannot meet the need of massive labeled samples for the training process. This work develops a triplet spectralwise transformer-based target detector (TSTTD) to deal with these problems. First, this work explores a novel triplet spectralwise transformer network for the HTD task, and a data augmentation method is used to construct sufficient and balanced training samples for balanced learning. The proposed network shows advantages in learning local features from multiple adjacent bands and global features with long-range dependence. Second, for improving the separability between the targets and backgrounds, a novel intercategory separation and intracategory aggregation (ISIA) loss function is proposed, which joints the hard-negative-mining (HNM) triplet loss and the binary cross-entropy (BCE) loss. Third, the experimental results on six datasets show that our proposed method is effective in leading to excellent detection performance when compared with other state-of-the-art methods.