Classifying hyperspectral images (HSIs) is a key challenge in remote sensing, with convolutional neural networks (CNNs) and transformer models becoming leading techniques in this area. CNNs, while effective, often struggle to adequately capture intricate semantic features, and increasing network depth leads to significantly higher computational costs. Conversely, transformers, despite their efficacy in modeling spectral-spatial dependencies, introduce significant computational overhead due to their complexity. Mamba, leveraging the state space model (SSM), presents a compelling alternative tha...
