# WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2408.01231 · HTML: https://mlsanity.com/paper/2408.01231/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Hyperspectral image analysis with Mamba, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Hyperspectral image analysis with Mamba](https://mlsanity.com/ssm/hyperspectral-image-analysis-with-mamba/)

## Builds on (most cited first, 13 of 13)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [SpectralMamba: Efficient Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.08489/) (2024)
- [Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1805.08620/) (2018)
- [Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18401/) (2024)
- [Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion](https://mlsanity.com/paper/2205.03742/) (2022)
- [3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12487/) (2024)
- [Pyramid Hierarchical Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.14945/) (2024)
- [Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2305.16682/) (2023)
- [Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.01095/) (2024)

## Built on by (most cited first, 11 of 11)

- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification](https://mlsanity.com/paper/2503.08239/) (2025)
- [Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification](https://mlsanity.com/paper/2411.18115/) (2024)
- [DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2412.17350/) (2024)
- [Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.06427/) (2025)
- [3D Fourier-based Global Feature Extraction for Hyperspectral Image Classification](https://mlsanity.com/paper/2603.16426/) (2026)
- [DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.01986/) (2025)
- [Dynamic Memory Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.13242/) (2025)
- [Cosine-Normalized Attention for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.01763/) (2026)
- [ConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.18856/) (2026)
- [Hyperspectral Image Models: Technical Report](https://mlsanity.com/paper/2609.39871/) (2026)

## Abstract (arXiv)

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.