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Hyperspectral remote sensing image classification based on domain-level complementarity of spatial-spectral

Huayue Chen1, Yuanyuan Li2, Bochuan Zheng2

  • 1School of Computer Science, China West Normal University Nanchong, 637002, China; Institute of Artificial Intelligence, the Key Laboratory of Optimization Theory and Applications, China West Normal University of Sichuan Province, Nanchong, 637002, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 21, 2026
PubMed
Summary

This study introduces D²FuPro, a new hyperspectral image classification method. It effectively integrates global and local spatial-spectral details, significantly improving classification accuracy by addressing homogeneity and heterogeneity issues.

Keywords:
Hyperspectral image classificationMesoscopic domain feature enhancementPanoramic domain low-rank feature extractionTensor singular value decomposition

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Image Processing

Background:

  • Hyperspectral images (HSI) face challenges like "homogeneity" and "heterogeneity," causing low intra-class consistency and poor inter-class differentiation.
  • Existing classification methods struggle to integrate global structure and local details of spatial-spectral information, impacting performance.
  • Addressing these issues is crucial for accurate HSI feature classification.

Purpose of the Study:

  • To propose a novel hyperspectral remote sensing image classification method, D²FuPro.
  • To leverage domain-level complementarity of spatial-spectral components for improved classification.
  • To enhance both intra-class consistency and inter-class differentiation in HSI classification.

Main Methods:

  • A dual-branching structure is employed to capture global (panoramic domain) and local (mesoscopic domain) spatial-spectral information.
  • Panoramic Domain Low-Rank Feature Extraction (PLFE) preserves global structure via low-rank modeling and texture smoothing.
  • Mesoscopic Domain Feature Enhancement (MDFE) optimizes local spectral information and spatial boundaries using neighboring pixel context.

Main Results:

  • The D²FuPro method effectively integrates dual-domain spatial-spectral information, achieving complementary benefits.
  • Experimental validation on four datasets shows superior performance compared to traditional and advanced methods.
  • The proposed method demonstrates significant improvements in classification accuracy.

Conclusions:

  • D²FuPro successfully addresses the homogeneity and heterogeneity issues in HSI classification.
  • The dual-domain approach enhances the integration of global and local spatial-spectral features.
  • This method offers a promising advancement for hyperspectral image classification accuracy.