WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image 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.

References in corpus (10)

• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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• Pyramid Hierarchical Transformer for Hyperspectral Image Classification
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Cited by in corpus (6)

• A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
• Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
• DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification
• Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
• Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification
• EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification