Subtle Spectral Difference Discriminative Deep Metric Learning With Spectral Center Construction for Hyperspectral Target Detection

Detection of targets for hyperspectral images (HSIs) persists as a fundamental task in remote sensing image processing. Exploring the discriminative ability of deep Siamese networks to distinguish targets from backgrounds is the mainstream method for target detection in HSIs. Nevertheless, these methods enhance the discriminative ability of networks by learning the separability distance between the target and the overall backgrounds, where the backgrounds are considered as a single category. As a result, they may struggle to effectively suppress backgrounds with solely subtle spectral differences from the target, resulting in a limited separability performance, and the inability to accurately detect the targets. To alleviate this problem, we propose a novel subtle spectral difference discriminative deep metric learning-based target detector for HSIs (denoted as S2D3ML) in this work. The proposed S2D3ML constructs a deep metric learning framework embedded with a discriminative constraint to learn a deep metric feature space for addressing limited separability, in which the subtle feature differences between targets and different ground objects can be distinguished. In addition, we investigate a new multi-block sparse representation score-based strategy to obtain sufficient samples and spectral centers of backgrounds for training the S2D3ML framework. Finally, the detection of targets is executed within the learned metric space. A comprehensive suite of experiments is rigorously conducted on four benchmark datasets, and the results indicate that the S2D3ML achieves superior performance in HSIs target detection.