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

Dehui Zhu,Yuetian Lu,Ping Zhong,Bo Du,Liangpei Zhang

Published 2025 in IEEE Transactions on Geoscience and Remote Sensing

ABSTRACT

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 (TD) 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 (S2D3ML)-based target detector for HSIs 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 multiblock 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 TD.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE Transactions on Geoscience and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/TGRS.2025.3639103
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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