# 3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification arXiv: https://arxiv.org/abs/2405.12487 ยท HTML: https://mlsanity.com/paper/2405.12487/ ## Key facts - Cited by 17 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 59 references that are in the map. - First posted on arXiv 21 May 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) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [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) - [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024) - [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024) - [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017) - [Long Range Language Modeling via Gated State Spaces](https://mlsanity.com/paper/2206.13947/) (2022) - [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021) - [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) - [Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/1703.07910/) (2017) - [Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.03577/) (2019) ## Built on by (most cited first, 17 of 17) - [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) - [WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01231/) (2024) - [IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.05100/) (2024) - [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) - [SITSMamba for Crop Classification based on Satellite Image Time Series](https://mlsanity.com/paper/2409.09673/) (2024) - [Sparse Deformable Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.09446/) (2025) - [MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.20509/) (2025) - [3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection](https://mlsanity.com/paper/2506.19263/) (2025) - [HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.15612/) (2025) - [MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture](https://mlsanity.com/paper/2507.04409/) (2025) - [Spatial-Temporal-Spectral Mamba with Sparse Deformable Token Sequence for Enhanced MODIS Time Series Classification](https://mlsanity.com/paper/2508.02839/) (2025) - [LGEST: Dynamic Spatial-Spectral Expert Routing for Hyperspectral Image Classification](https://mlsanity.com/paper/2603.24045/) (2026) - [Physics-Aligned Spectral Mamba: Decoupling Semantics and Dynamics for Few-Shot Hyperspectral Target Detection](https://mlsanity.com/paper/2604.05562/) (2026) - [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026) ## Abstract (arXiv) Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional Neural Networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, respectively. Fortunately, recent Mamba architectures built upon the State Space Model integrate the advantages of long-range sequence modeling and linear computational efficiency, exhibiting substantial potential in low-dimensional scenarios. Motivated by this, we propose a novel 3D-Spectral-Spatial Mamba (3DSS-Mamba) framework for HSI classification, allowing for global spectral-spatial relationship modeling with greater computational efficiency. Technically, a spectral-spatial token generation (SSTG) module is designed to convert the HSI cube into a set of 3D spectral-spatial tokens. To overcome the limitations of traditional Mamba, which is confined to modeling causal sequences and inadaptable to high-dimensional scenarios, a 3D-Spectral-Spatial Selective Scanning (3DSS) mechanism is introduced, which performs pixel-wise selective scanning on 3D hyperspectral tokens along the spectral and spatial dimensions. Five scanning routes are constructed to investigate the impact of dimension prioritization. The 3DSS scanning mechanism combined with conventional mapping operations forms the 3D-spectral-spatial mamba block (3DMB), enabling the extraction of global spectral-spatial semantic representations. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on HSI classification benchmarks.