DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2502.01986v1
• Date: Tue, 04 Feb 2025 04:00:08 GMT
• Title: DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification
• Authors: Weijia Cao, Xiaofei Yang, Yicong Zhou, Zheng Zhang,
• Abstract summary: Hyperspectral image classification presents challenges due to spectral redundancy and complex spatial-spectral dependencies.<n>This paper proposes a novel framework, DCT-Mamba3D, for hyperspectral image classification.
• Score: 38.538268270711534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral image classification presents challenges due to spectral redundancy and complex spatial-spectral dependencies. This paper proposes a novel framework, DCT-Mamba3D, for hyperspectral image classification. DCT-Mamba3D incorporates: (1) a 3D spectral-spatial decorrelation module that applies 3D discrete cosine transform basis functions to reduce both spectral and spatial redundancy, enhancing feature clarity across dimensions; (2) a 3D-Mamba module that leverages a bidirectional state-space model to capture intricate spatial-spectral dependencies; and (3) a global residual enhancement module that stabilizes feature representation, improving robustness and convergence. Extensive experiments on benchmark datasets show that our DCT-Mamba3D outperforms the state-of-the-art methods in challenging scenarios such as the same object in different spectra and different objects in the same spectra.

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