AGMS: Adversarial Sample Generation-Based Multiscale Siamese Network for Hyperspectral Target Detection

Hyperspectral target detection (HTD) has been a critical issue in the field of Earth observation, with widespread applications in both military and civilian domains. However, existing deep learning-based HTD methods are hindered due to insufficient and low-quality prior training samples, as well as inadequate background suppression capabilities. To address these issues, this article proposes an adversarial sample generation-based multiscale Siamese network (AGMS) for HTD. First, the AGMS utilizes the idea of generative adversarial learning based on the prior few targets and diverse backgrounds to generate adversarial target-background sample pairs, thereby producing high-quality training samples, which enhances the distinctiveness between the target and background samples by adversarially training the generator to produce the target/background samples. In addition, to further highlight the targets and suppress the backgrounds, a difference amplification loss and an adaptive weighted binary cross-entropy loss are proposed. Finally, a multiscale convolutional Siamese network model is designed to explore the generated spectral information at multiple levels and achieve target detection through contrastive learning. Numerous experimental results on four real HSI datasets verify the superiority of the AGMS in comparison to many classical and recently proposed HTD methods. The codes are available at https://github.com/ShissHAN/AGMS.