HyPyraMamba: A Pyramid Spectral Attention and Mamba-Based Architecture for Robust Hyperspectral Image Classification

In hyperspectral image (HSI) classification, the high-dimensionality and the complex coupling of spatial-spectral features present severe challenges to existing deep learning methods in terms of accuracy, generalization, and computational efficiency. Researchers have recently explored CNN and Transformer-based methods to overcome these limitations, but CNN's limited receptive field prevents effective modeling of long-range dependencies, while Transformers suffer from high computational cost and inefficiency in high-dimensional data. Motivated by these limitations, the state-space model (SSM) Mamba shows great potential as an efficient alternative for sequence and dependency modeling. Building on this foundation, we propose HyPyraMamba, a novel architecture designed to effectively overcome the above challenges. It integrates the Pyramid Spectral Attention (PSA) module to capture multi-scale key spectral features, thereby reducing interference caused by spectral redundancy. We developed an Adaptive Expert Depthwise Convolution (AEDC) module that enhances the model's ability to express multi-scale spatial-spectral features, and a sequence modeling module, Mamba. In the Mamba module, we utilize the spatial Mamba and spectral Mamba branches to enhance spatial structure and spectral correlation modeling. Extensive experiments on four benchmark HSI datasets demonstrate that HyPyraMamba significantly outperforms several recent state-of-the-art methods and provides a favorable accuracy–efficiency trade-off. In particular, class-wise analyses on spectrally similar land-cover categories (e.g., different soybean and bareland types) show that HyPyraMamba markedly reduces mutual confusion compared with CNN-, Transformer-, and Mamba-based baselines. The code will be available at https://github.com/dekai-li/HyPyraMamba.