HyperMamba: A Spectral-Spatial Adaptive Mamba for Hyperspectral Image Classification

Transformers have significantly advanced hyperspectral image (HSI) classification through their proficiency in modeling long sequences. However, the high dimensionality of HSIs poses a particular challenge for Transformers due to their quadratic computational complexity. In natural language processing, state-space models (SSMs) such as Mamba hold great promise for handling long sequence tasks with significantly reduced computational overhead. However, the original Mamba lacks consideration for the spectral and spatial information inherent in HSIs. Inspired by this, we propose the HyperMamba, a novel spectral-spatial adaptive Mamba for HSI classification. The core idea of HyperMamba involves adaptively scanning spatial neighborhood pixels and dynamically enhancing spectral bands for spectral scanning based on acquired spatial neighborhood information. Specifically, HyperMamba consists of two core modules: the spatial neighborhood adaptive scanning (SNAS) module and the spectral adaptive enhancement scanning (SAES) module. Initially, the SNAS module analyzes the spectral characteristics of classified pixels, adaptively selecting the optimal neighborhood for spatial scanning by balancing spatial neighborhood information and local spatial structure. Subsequently, the SAES module dynamically enhances the spectral features of classified pixels using neighborhood spectral information and conducts spectral scanning. Finally, the spectral features of the target pixels are fed into a single fully connected layer classifier, achieving high-precision HSI classification. Extensive experiments demonstrate the effectiveness of HyperMamba, surpassing state-of-the-art methods across three widely used HSI datasets. The code will be available at https://github.com/chiangliu/HyperMamba.