Clustering-Guided Spatial-Spectral Mamba for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2601.16098v1
• Date: Thu, 22 Jan 2026 16:47:07 GMT
• Title: Clustering-Guided Spatial-Spectral Mamba for Hyperspectral Image Classification
• Authors: Zack Dewis, Yimin Zhu, Zhengsen Xu, Mabel Heffring, Saeid Taleghanidoozdoozan, Quinn Ledingham, Lincoln Linlin Xu,
• Abstract summary: This paper presents CSSMamba (Clustering-guided Spatial-Spectral Mamba) framework to better address the challenges.<n>To achieve efficient and adaptive token sequences for improved Mamba performance, we integrate the clustering mechanism into a spatial Mamba architecture.<n>Experiments on the Pavia University, Indian Pines, and Liao-Ning 01 datasets demonstrate that CSSMamba achieves higher accuracy and better boundary preservation.
• Score: 2.320960021702736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although Mamba models greatly improve Hyperspectral Image (HSI) classification, they have critical challenges in terms defining efficient and adaptive token sequences for improve performance. This paper therefore presents CSSMamba (Clustering-guided Spatial-Spectral Mamba) framework to better address the challenges, with the following contributions. First, to achieve efficient and adaptive token sequences for improved Mamba performance, we integrate the clustering mechanism into a spatial Mamba architecture, leading to a cluster-guided spatial Mamba module (CSpaMamba) that reduces the Mamba sequence length and improves Mamba feature learning capability. Second, to improve the learning of both spatial and spectral information, we integrate the CSpaMamba module with a spectral mamba module (SpeMamba), leading to a complete clustering-guided spatial-spectral Mamba framework. Third, to further improve feature learning capability, we introduce an Attention-Driven Token Selection mechanism to optimize Mamba token sequencing. Last, to seamlessly integrate clustering into the Mamba model in a coherent manner, we design a Learnable Clustering Module that learns the cluster memberships in an adaptive manner. Experiments on the Pavia University, Indian Pines, and Liao-Ning 01 datasets demonstrate that CSSMamba achieves higher accuracy and better boundary preservation compared to state-of-the-art CNN, Transformer, and Mamba-based methods.

Related papers

• Samba+: General and Accurate Salient Object Detection via A More Unified Mamba-based Framework [66.2103745798444]
  Saliency Mamba (Samba) is a pure Mamba-based architecture that flexibly handles various distinct salient object detection tasks.<n>Samba individually outperforms existing methods across six SOD tasks on 22 datasets with lower computational cost.<n>Samba+ achieves even superior results on these tasks and datasets by using a single trained versatile model.
  arXiv  Detail & Related papers  (2026-02-02T03:34:25Z)
• MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification [46.67137351665963]
  Mamba-based models have recently demonstrated significant potential in hyperspectral image (HSI) classification.<n>We propose MambaMoE, a novel spectral-spatial Mixture-of-Experts (MoE) framework, which represents the first MoE-based approach in the HSI classification domain.<n>MambaMoE achieves state-of-the-art performance in both classification accuracy and computational efficiency compared to existing advanced methods.
  arXiv  Detail & Related papers  (2025-04-29T07:50:36Z)
• Sparse Deformable Mamba for Hyperspectral Image Classification [1.3471768511567523]
  Mamba models significantly improve hyperspectral image (HSI) classification.<n>One critical challenge is the difficulty in building the sequence of Mamba tokens efficiently.<n>This paper presents a Sparse Deformable Mamba (SDMamba) approach for enhanced HSI classification.
  arXiv  Detail & Related papers  (2025-04-13T06:08:19Z)
• MambaGlue: Fast and Robust Local Feature Matching With Mamba [9.397265252815115]
  We propose a novel Mamba-based local feature matching approach, called MambaGlue.<n>Mamba is an emerging state-of-the-art architecture rapidly gaining recognition for its superior speed in both training and inference.<n>Our MambaGlue achieves a balance between robustness and efficiency in real-world applications.
  arXiv  Detail & Related papers  (2025-02-01T15:43:03Z)
• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision.<n>In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning [54.19222454702032]
  Continual Learning aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge.<n>State Space Models (SSMs) have achieved notable success in computer vision.<n>We introduce Mamba-CL, a framework that continuously fine-tunes the core SSMs of the large-scale Mamba foundation model.
  arXiv  Detail & Related papers  (2024-11-23T06:36:16Z)
• ReMamba: Equip Mamba with Effective Long-Sequence Modeling [50.530839868893786]
  We propose ReMamba, which enhances Mamba's ability to comprehend long contexts.<n>ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process.
  arXiv  Detail & Related papers  (2024-08-28T02:47:27Z)
• MambaVision: A Hybrid Mamba-Transformer Vision Backbone [54.965143338206644]
  We propose a novel hybrid Mamba-Transformer backbone, MambaVision, specifically tailored for vision applications.<n>We show that equipping the Mamba architecture with self-attention blocks in the final layers greatly improves its capacity to capture long-range spatial dependencies.<n>For classification on the ImageNet-1K dataset, MambaVision variants achieve state-of-the-art (SOTA) performance in terms of both Top-1 accuracy and throughput.
  arXiv  Detail & Related papers  (2024-07-10T23:02:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.