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Hyperspectral images (HSI) captured via remote sensing systems contain numerous consecutive narrow spectral bands, offering rich spatial-spectral data across the electromagnetic spectrum. Given their distinct benefits, HSI has gained significant interest and found broad utilization in domains like military surveillance, urban planning, biochemical analysis, wildfire monitoring, and object identification.

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Self-Supervised Graph Convolutional with Locality Preserving Low-Pass Embedding for Large-Scale Hyperspectral Image Clustering

  • 章节

  • 发表日期：

    2025年

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  • Yao Ding,
  • Zhili Zhang,
  • Haojie Hu,
  • Renxiang Guan,
  • Jie Feng,
  • Zhiyong Lv

摘要

Hyperspectral images (HSI) captured via remote sensing systems contain numerous consecutive narrow spectral bands, offering rich spatial-spectral data across the electromagnetic spectrum. Given their distinct benefits, HSI has gained significant interest and found broad utilization in domains like military surveillance, urban planning, biochemical analysis, wildfire monitoring, and object identification.