WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification

TL;DR

WaveMamba is a novel hyperspectral image classification model that combines wavelet transformation with a spatial-spectral Mamba architecture, improving accuracy and capturing detailed spatial and spectral features efficiently.

Contribution

It introduces a wavelet-enhanced spatial-spectral Mamba model that effectively captures local and global features for hyperspectral image classification.

Findings

Achieves 4.5% higher accuracy on University of Houston dataset

Achieves 2.0% higher accuracy on Pavia University dataset

Outperforms existing models in hyperspectral classification

Abstract

Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI classification, challenges such as computational efficiency and the need for extensive labeled data persist. This paper introduces WaveMamba, a novel approach that integrates wavelet transformation with the spatial-spectral Mamba architecture to enhance HSI classification. WaveMamba captures both local texture patterns and global contextual relationships in an end-to-end trainable model. The Wavelet-based enhanced features are then processed through the state-space architecture to model spatial-spectral relationships and temporal dependencies. The experimental results indicate that WaveMamba surpasses existing models, achieving an accuracy improvement of 4.5\% on the University of Houston dataset and a 2.0\% increase on the Pavia University dataset.