Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2604.09948v1
• Date: Fri, 10 Apr 2026 22:56:49 GMT
• Title: Unmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification
• Abstract summary: This letter presents a novel unmixing-guided spatial-spectral Mamba with clustering tokens for improved HSI classification.<n>To disentangle the spectral mixture effect in HSI for improved pattern discovery, we design a novel spectral unmixing network.<n>To generate Mamba token sequences, based on the clusters defined by abundance maps, we design an efficient Top-textitK token selection strategy.<n>Third, to improve spatial-spectral feature learning and detail preservation, based on the Top-textitK token sequences, we design a novel unmixing-guided spatial-spectral Mamba module
• Score: 2.1741810474011047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although hyperspectral image (HSI) classification is critical for supporting various environmental applications, it is a challenging task due to the spectral-mixture effect, the spatial-spectral heterogeneity and the difficulty to preserve class boundaries and details. This letter presents a novel unmixing-guided spatial-spectral Mamba with clustering tokens for improved HSI classification, with the following contributions. First, to disentangle the spectral mixture effect in HSI for improved pattern discovery, we design a novel spectral unmixing network that not only automatically learns endmembers and abundance maps from HSI but also accounts for endmember variabilities. Second, to generate Mamba token sequences, based on the clusters defined by abundance maps, we design an efficient Top-\textit{K} token selection strategy to adaptively sequence the tokens for improved representational capability. Third, to improve spatial-spectral feature learning and detail preservation, based on the Top-\textit{K} token sequences, we design a novel unmixing-guided spatial-spectral Mamba module that greatly improves traditional Mamba models in terms of token learning and sequencing. Fourth, to learn simultaneously the endmember-abundance patterns and classification labels, a multi-task scheme is designed for model supervision, leading to a new unmixing-classification framework that outputs not only accurate classification maps but also a comprehensive spectral-library and abundance maps. Comparative experiments on four HSI datasets demonstrate that our model can greatly outperform the other state-of-the-art approaches. Code is available at https://github.com/GSIL-UCalgary/Unmixing_guided_Mamba.git

Related papers

• Hyperbolic Hierarchical Clustering for Visual Representation Learning [62.38996677789715]
  An effective token mixer is a fundamental component of modern vision backbones like Transformers.<n>We introduce ClusterMixer, a transparent token mixer grounded in a clustering paradigm and interpretable by design.<n>We present HCFormer, a new backbone architecture that integrates ClusterMixer with a series of meticulously designed clustering strategies.
  arXiv  Detail & Related papers  (2026-08-24T00:01:09Z)
• Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations [53.66343110413339]
  A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced.<n>The approach combines scalable geospatial representations with an interpretable quantum mask, enabling both binary and multiclass classification.
  arXiv  Detail & Related papers  (2026-07-21T18:30:50Z)
• mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification [2.3379613890114395]
  This paper presents a clustering-guided mHC Mamba model (CalgarymHC-HSI) for enhanced HSI classification.<n>The proposed approach is tested on benchmark datasets in comparison with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-03-03T18:56:40Z)
• Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering [73.01141916544103]
  Hyperspectral image (HSI) clustering groups pixels into clusters without labeled data.<n>Most methods rely on superpixel segmentation and perform superpixel-level clustering based on graph neural networks (GNNs)<n>We propose a structural-spectral graph convolutional operator (SSGCO) tailored for graph-structured HSI superpixels to improve representation quality.
  arXiv  Detail & Related papers  (2025-06-11T16:41:34Z)
• 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)
• MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification [46.111607032455225]
  We propose a novel HSI classification model based on a Mamba model, named MambaHSI.<n> Specifically, we design a spatial Mamba block (SpaMB) to model the long-range interaction of the whole image at the pixel-level.<n>We propose a spectral Mamba block (SpeMB) to split the spectral vector into multiple groups, mine the relations across different spectral groups, and extract spectral features.
  arXiv  Detail & Related papers  (2025-01-09T03:27:47Z)
• Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification [27.943537708598306]
  We propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba)<n>MorpMamba combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers.<n> Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models.
  arXiv  Detail & Related papers  (2024-08-02T16:28:51Z)
• Spectral-Spatial Mamba for Hyperspectral Image Classification [23.215920983979426]
  spectral-spatial Mamba (SS-Mamba) is applied to hyperspectral image (HSI) classification. The proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results.
  arXiv  Detail & Related papers  (2024-04-29T03:36:05Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)

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.