THANTD: Triplet Hybrid Attention Network for Hyperspectral Target Detection - Ju Liu; Haoqian Wang; Xiangai Cheng; Zhongyang Xing; Zhongjie Xu (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Hyperspectral images (HSIs), characterized by their rich spatial–spectral information, have been widely utilized in hyperspectral target detection (HTD), a key area within hyperspectral remote sensing information processing. In recent years, deep learning has made significant progress in the HTD field, leading to the development of many effective algorithms. However, as a data-driven approach, deep learning methods still face challenges, such as the lack of sufficient labeled samples for training and the difficulty of capturing the diversity of spectral variations. In addition, HSIs often contain hundreds of bands, making it essential to model and learn interband correlations effectively. To address these challenges, we propose a triplet hybrid attention network for HTD (THANTD). It features a backbone with three parallel, weight-sharing hybrid attention blocks that integrate the multihead self-attention mechanism and the channel attention module. This structure captures both global spectral features and relationships between spectral channels. Utilizing a coarse detection and radiation transmission model for data augmentation, we introduce a method to construct positive and negative samples, enabling better selection of pure target and background samples, which simulates real-world spectral variations to support triplet network training. Furthermore, a spectral grouping strategy is used to extract features from different narrow bands. We also developed an enhanced triplet-binary cross entropy loss (BCE Loss) (ETB-Loss), which combines dual triplet loss and BCE Loss to more effectively distinguish between positive and negative samples and enhance supervision during training. Extensive experiments on five datasets verify the effectiveness and superiority of the proposed THANTD across various evaluation metrics.