CS-TTD: Triplet Transformer for Compressive Hyperspectral Target Detection

Qingyao Yang, Xiaoqin Wang, Long Chen, Yumei Zhou, Shushan Qiao

Abstract: Detecting targets in hyperspectral image under compressive sensing (CS) is crucial for real-time applications in front ends. However, applying the existing deep learning (DL) methods directly to compressed hyperspectral image has been challenging, including the uncertainty introduced by the random sensing matrix in the CS acquisition model, and the ineffectuality of traditional similarity metrics. In this article, we present a solution to these challenges by introducing a CS-based triplet transformer detector (CS-TTD). Our approach involves data augmentation using the restricted distribution property (RDP) to generate samples for a balanced number of positive samples, followed by the Siamese network with a triplet transformer to transition spectral vectors from the compressed domain to an embedding space. In addition, we introduce a combined convolution network (CCN) classification module to replace traditional metrics for obtaining classification results. To improve the training and make full use of dataset labels, we also present a two-stage training approach, known as intercategory separation and intracategory aggregation (ISIA), combined with hard-negative mining (HNM) and semi-HNM. Besides, to estimate the minimal number of compressed bands for sufficient detection performance, we introduce a kernel distribution estimation-based sequential iteration (KDE-SI). Our method achieves hyperspectral target detection (HTD) without reconstruction, and experimental results demonstrate that its performance at 0.16 compression ratio (CR) is comparative to the existing methods in the original domain. The code for this work is available at https://github.com/nicyyyy/CS-TTD.git.