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

TL;DR

A novel clustering-guided mHC Mamba model (mHC-HSI) is presented, based on the mHC framework, that explicitly learns both spatial and spectral information in HSI to improve spatial-spectral feature learning and enhance explainability.

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 (4)

• Figure 1: Figure from xie2026mhc. Comparison between (a) a standard residual connection and (b) the Manifold-Constrained Hyper-Connections (mHC) 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 mHC framework for hyperspectral image classification with improved accuracy and interpretability.
• Figure 2: The architecture of the proposed clustering-guided mHC Mamba (mHC-HSI) model. Based on the mHC 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 $x_l$.
• Figure 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) mHC-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.
• Figure 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.