MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification

2025-01-09

IEEE Transactions on Geoscience and Remote Sensing

AI Summary

To address the high computational cost of Transformers and the difficulty of existing Mamba architectures in jointly modeling spatial and spectral structures in hyperspectral image (HSI) classification, this paper proposes HS-Mamba—the first full-image-level Mamba architecture for HSI. Methodologically, it introduces (1) decoupled Spatial Mamba Blocks (SpaMB) and Spectral Mamba Blocks (SpeMB) to capture long-range spatial dependencies and grouped spectral correlations, respectively; (2) a Spatial-Spectral Fusion Module (SSFM) for adaptive cross-domain feature interaction; and (3) linear-complexity state-space modeling throughout the network to balance efficiency and representational capacity. Evaluated on four benchmark HSI datasets—including Indian Pines—HS-Mamba achieves state-of-the-art accuracy with significantly fewer parameters and lower FLOPs than CNNs, Transformers, and prior Mamba variants, demonstrating Mamba’s feasibility and superiority as an efficient backbone for HSI classification.

Technology Category

• Machine Learning: Learning with Manifolds
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Transformer has been extensively explored for hyperspectral image (HSI) classification. However, transformer poses challenges in terms of speed and memory usage because of its quadratic computational complexity. Recently, the Mamba model has emerged as a promising approach, which has strong long-distance modeling capabilities while maintaining a linear computational complexity. However, representing the HSI is challenging for the Mamba due to the requirement for an integrated spatial and spectral understanding. To remedy these drawbacks, we propose a novel HSI classification model based on a Mamba model, named MambaHSI, which can simultaneously model long-range interaction of the whole image and integrate spatial and spectral information in an adaptive manner. Specifically, we design a spatial Mamba block (SpaMB) to model the long-range interaction of the whole image at the pixel-level. Then, 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. Finally, we propose a spatial-spectral fusion module (SSFM) to adaptively integrate spatial and spectral features of a HSI. To our best knowledge, this is the first image-level HSI classification model based on the Mamba. We conduct extensive experiments on four diverse HSI datasets. The results demonstrate the effectiveness and superiority of the proposed model for HSI classification. This reveals the great potential of Mamba to be the next-generation backbone for HSI models. Codes are available at https://github.com/li-yapeng/MambaHSI.

Problem

• High Spectral Imaging
• Transformer Networks
• Mamba Model

Innovation

• MambaHSI
• Spatial-Spectral Fusion
• High Spectral Imaging Classification