mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification

Yimin Zhu; Zack Dewis; Quinn Ledingham; Saeid Taleghanidoozdoozan; Mabel Heffring; Zhengsen Xu; Motasem Alkayid; Megan Greenwood

cs.CVMar 3, 2026

mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification

Authors: Yimin Zhu, Zack Dewis, Quinn Ledingham, Saeid Taleghanidoozdoozan, Mabel Heffring, Zhengsen Xu, Motasem Alkayid, Megan Greenwood, +1 more

Organizations: Department of Geomatics Engineering, University of Calgary, Canada · Department of Geography, Faculty of Arts, The University of Jordan, Amman, Jordan

Abstract

Recently, DeepSeek has invented the manifold-constrained hyper-connection (mHC) approach which has demonstrated significant improvements over the traditional residual connection in deep learning models \cite{xie2026mhc}. Nevertheless, this approach has not been tailor-designed for improving hyperspectral image (HSI) classification. This paper presents a clustering-guided mHC Mamba model (mHC-HSI) for enhanced HSI classification, with the following contributions. First, to improve spatial-spectral feature learning, we design a novel clustering-guided Mamba module, based on the mHC framework, that explicitly learns both spatial and spectral information in HSI. Second, to decompose the complex and heterogeneous HSI into smaller clusters, we design a new implementation of the residual matrix in mHC, which can be treated as soft cluster membership maps, leading to improved explainability of the mHC approach. Third, to leverage the physical spectral knowledge, we divide the spectral bands into physically-meaningful groups and use them as the "parallel streams" in mHC, leading to a physically-meaningful approach with enhanced interpretability. The proposed approach is tested on benchmark datasets in comparison with the state-of-the-art methods, and the results suggest that the proposed model not only improves the accuracy but also enhances the model explainability. Code is available here: https://github.com/GSIL-UCalgary/mHC_HyperSpectral

Figures & tables

Fig. 1: Figure from [ 1 ] . Comparison between (a) a standard residual connection and (b) the Manifold-Constrained Hyper-Connections ( m HC) framework. The three red bounding boxes in (b) highlight the key components where our contributions are introduced. Specifically, they correspond to: (1) a clustering-guided Mamba module for enhanced spatial–spectral feature learning in hyperspectral images, (2) a novel residual mapping implementation that can be interpreted as soft cluster membership maps to decompose heterogeneous scenes and improve model explainability, and (3) a physically meaningful multi-stream design based on spectral band grouping to incorporate spectral prior knowledge. Together, these modifications adapt the original m HC framework for hyperspectral image classification with improved accuracy and interpretability.

Fig. 2: The architecture of the proposed clustering-guided m HC Mamba ( m HC-HSI) model. Based on the m HC framework, we implement $F(\cdot)$ with a novel clustering-guided Mamba module, which has a spectral Mamba module followed by the clustering-guided spatial Mamba module. Second, we implement the residual matrix $\mathcal{H}^{\text{res}}$ as soft cluster membership maps, which are used to decompose the complex and heterogeneous HSI into smaller clusters in the clustering-guided spatial Mamba. Third, we divide the input the spectral bands in the input HSI into physically-meaningful groups and use them as the ”streams” in $x_{l}$ .

Class No.	Train Number	Test Number	CNN-based		GAN-based	Transformer-based			Mamba-based		m HC-based
Class No.	Train Number	Test Number	SSRN	SS-ConvNeXt	MTGAN	SSFTT	SSTN	GSC-ViT	MambaHSI	3DSS-Mamba	Ours
1	5	37	90.98	84.21	84.63	94.18	94.44	94.59	94.74	95.12	100.00
2	143	1142	97.54	99.53	97.20	94.9	97.99	95.19	98.08	93.22	99.13
3	83	664	96.88	96.62	95.9	92.16	95.48	96.54	96.36	93.44	98.24
4	24	189	97.7	95.24	96.85	94.8	93.12	99.47	90.05	96.71	100.00
5	48	387	95.13	97.91	95.61	95.33	99.22	98.45	97.67	96.71	95.82

TABLE I: Quantitative performance of different classification methods in terms of OA, AA,

k

, as well as the accuracies for each class on the Indian Pines dataset with 10 % training samples. The best results are in bold and colored shadow.

Panel of Figure 4 — Fig. 3: The Indian Pines classification map generated by different methods. (a) SSRN (b) SS-ConvNeXt (c) MTGAN (d) SSFTT (e) SSTN (f) GSC-ViT (g) MambaHSI (h) 3DSS-Mamba (i) m HC-HSI (j) False Color Image (k) Ground Truth. Some red circles are shown on the RGB image to illustrate the boundary preservation of our proposed model.

Metrics	Expansion rate $n$ =2	Expansion rate $n$ =4
OA	98.95	98.54
AA	97.65	97.77
Kappa	95.98	95.80

TABLE II: Impact on the expansion rate

n

Fig. 4: Visualization of the interpretable $\mathcal{H}^{\text{res}}$ with overlaid class boundaries. The boundary is selected based on the highest mean value for each class boundary. White text shows the name of the category.

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Apr 10, 2026J/K move · Enter open · S save

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

Yimin Zhu, Lincoln Linlin Xu

Department of Geomatics Engineering, University of Calgary, Canada

Hyperspectral Image Classification Hyperspectral Imaging Mamba

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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-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-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

Abstract

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