# Spectral-Spatial Mamba for Hyperspectral Image Classification arXiv: https://arxiv.org/abs/2404.18401 ยท HTML: https://mlsanity.com/paper/2404.18401/ ## Key facts - Cited by 24 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 8 of its 68 references that are in the map. - First posted on arXiv 29 Apr 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, 8 of 8) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021) - [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022) - [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021) - [Earthformer: Exploring Space-Time Transformers for Earth System Forecasting](https://mlsanity.com/paper/2207.05833/) (2022) - [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019) - [Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods](https://mlsanity.com/paper/1310.5107/) (2013) - [Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning](https://mlsanity.com/paper/1812.09530/) (2018) ## Built on by (most cited first, 24 of 24) - [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024) - [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025) - [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024) - [DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2406.07050/) (2024) - [WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01231/) (2024) - [RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing](https://mlsanity.com/paper/2503.10392/) (2025) - [Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01224/) (2024) - [Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01372/) (2024) - [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025) - [MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.20509/) (2025) - [MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation](https://mlsanity.com/paper/2502.17255/) (2025) - [Onboard Hyperspectral Super-Resolution with Deep Pushbroom Neural Network](https://mlsanity.com/paper/2507.20765/) (2025) - [Scalable neural pushbroom architectures for real-time denoising of hyperspectral images onboard satellites](https://mlsanity.com/paper/2601.05020/) (2026) - [Cross-Scan Mamba with Masked Training for Robust Spectral Imaging](https://mlsanity.com/paper/2408.00629/) (2024) - [HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.15612/) (2025) - [CSFMamba: Cross State Fusion Mamba Operator for Multimodal Remote Sensing Image Classification](https://mlsanity.com/paper/2509.00677/) (2025) - [ConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.18856/) (2026) - [A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.23622/) (2026) - [Quantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis](https://mlsanity.com/paper/2606.17222/) (2026) - [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026) - [DAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2607.15128/) (2026) - [MSCM-net: A hyperspectral image classiffcation method based on multi-scale convolution and Mamba](https://mlsanity.com/paper/2607.28277/) (2026) - [Token Clustering and Semantic Sequence Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2609.28580/) (2026) - [Hyperspectral Image Models: Technical Report](https://mlsanity.com/paper/2609.39871/) (2026) ## Abstract (arXiv) Recently, deep learning models have achieved excellent performance in hyperspectral image (HSI) classification. Among the many deep models, Transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, Transformer has the problem of quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of Transformers. Therefore, in this paper, we make a preliminary attempt to apply the Mamba to HSI classification, leading to the proposed spectral-spatial Mamba (SS-Mamba). Specifically, the proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. Firstly, the token generation module converts any given HSI cube to spatial and spectral tokens as sequences. And then these tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB block consists of two basic mamba blocks and a spectral-spatial feature enhancement module. The spatial and spectral tokens are processed separately by the two basic mamba blocks, respectively. Besides, the feature enhancement module modulates spatial and spectral tokens using HSI sample's center region information. In this way, the spectral and spatial tokens cooperate with each other and achieve information fusion within each block. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results compared with the state-of-the-art methods. The Mamba-based method opens a new window for HSI classification.