Spectral-Spatial Mamba for Hyperspectral Image Classification

arXiv:2404.18401v1 Announce Type: new
Abstract: Recently, deep learning models have achieved excellent performance in hyperspectral image (HSI) classification. Among the many deep models, Transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, Transformer has the problem of quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational …

abstract arxiv attention classification complexity computational cs.cv deep learning dependencies features however image mamba modeling performance self-attention spatial transformer type