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December 9, 2025Remote SensingOpen Access

Discriminative Anchor Learning for Hyperspectral Image Clustering

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Authors

YYYu YunXidian UniversityQGQuanxue GaoXidian UniversityJZJianwei ZhaoJiangnan University

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Overview

Algorithm improves clustering performance of hyperspectral images by optimizing anchor distributions, suggesting better remote sensing applications.

Key Points

  • This research aims to enhance clustering performance in hyperspectral images by improving anchor quality.
  • Proposed a discriminative anchor-based hyperspectral image clustering algorithm
  • Shared the coefficient matrix among anchors and samples
  • Imposed low-rank and probabilistic constraints on the consensus coefficient matrix
  • Demonstrated the superiority and effectiveness of the proposed method through extensive experiments
  • Improved anchor quality and reduced clustering degradation in hyperspectral imaging

Cite This Study

Yun et al. (2025) studied this question.

synapsesocial.com/papers/69401d5b2d562116f28f8d11https://doi.org/10.3390/rs17243969
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