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

TL;DR

DCT-Mamba3D is a novel hyperspectral image classification framework that combines spectral decorrelation, spatial-spectral dependency modeling, and residual enhancement to improve accuracy and robustness.

Contribution

It introduces a new spectral-spatial decorrelation module and a bidirectional state-space model for better feature extraction in hyperspectral images.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively handles spectral redundancy and complex dependencies.

Improves robustness and convergence in classification tasks.

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.