Efficient Spatial-Spectral Feature Extraction in Hyperspectral Images via Holistic Multivariance Decomposition

2026-07-24

AI Summary

This work addresses the limitations of traditional tensor decomposition methods in effectively capturing the complex spatial-spectral coupling and cross-domain dependencies inherent in hyperspectral images. To this end, we propose a holistic multi-variate tensor decomposition framework that, for the first time, explicitly models both isolated spatial-spectral features and high-dimensional collaborative interactions while preserving essential joint multi-variate structures even under strong subspace compression. By leveraging a structurally flexible tensor algorithm, our approach overcomes the low-rank approximation constraints of conventional Tucker and CP decompositions, enabling highly discriminative feature extraction. Extensive experiments on four benchmark hyperspectral datasets demonstrate that the proposed method significantly outperforms existing decomposition techniques and consistently enhances the classification accuracy of various supervised learning algorithms.

Abstract

Tensor decomposition serves as a foundational tool for feature extraction in hyperspectral image classification, a domain classically dominated by the Tucker and Canonical Polyadic decompositions. Although widely adopted, these schemes often struggle to fully encapsulate the deeply coupled geometric and intrinsic structures inherent to multidimensional hyperspectral data. Their structural reliance on rigid low-rank approximations successfully captures independent mode variations but systematically neglects complex, cross-domain spatial and spectral interdependencies. To overcome this limitation, we introduce the Holistic Multivariance Decomposition framework to achieve highly discriminative hyperspectral feature extraction. The Holistic Multivariance Decomposition provides a novel, structurally flexible tensor algorithm that explicitly models isolated spatial and spectral behaviors, alongside intricate, higher dimensional cooperative interactions. Comprehensive experimental evaluations across four benchmark hyperspectral datasets demonstrate that the proposed Holistic Multivariance Decomposition approximants consistently yield superior classification accuracy compared to conventional Tucker and Canonical Polyadic decomposition methods across diverse supervised learning algorithms. By effectively preserving essential joint multivariance features even under severe subspace compression, these results establish the Holistic Multivariance Decomposition as a robust, high fidelity computational framework for resolving complex multidimensional data structures in hyperspectral analysis.

Problem

• hyperspectral image classification
• tensor decomposition
• spatial-spectral interdependencies
• multivariance features
• feature extraction

Innovation

• Holistic Multivariance Decomposition
• hyperspectral image classification
• tensor decomposition
• spatial-spectral feature extraction
• multivariance modeling