Hyperspectral image (HSI) classification has been one of the hot topics in
remote sensing fields. Recently, the Mamba architecture based on selective
state-space models (S6) has demonstrated great advantages in long sequence
modeling. However, the unique properties of hyperspectral data, such as high
dimensionality and feature inlining, pose challenges to the application of
Mamba to HSI classification. To compensate for these shortcomings, we propose
an full-field interaction multi-groups Mamba framework (HS-Mamba), which adopts
a strategy different from pixel-patch based or whole-image based, but combines
the advantages of both. The patches cut from the whole image are sent to
multi-groups Mamba, combined with positional information to perceive local
inline features in the spatial and spectral domains, and the whole image is
sent to a lightweight attention module to enhance the global feature
representation ability. Specifically, HS-Mamba consists of a dual-channel
spatial-spectral encoder (DCSS-encoder) module and a lightweight global inline
attention (LGI-Att) branch. The DCSS-encoder module uses multiple groups of
Mamba to decouple and model the local features of dual-channel sequences with
non-overlapping patches. The LGI-Att branch uses a lightweight compressed and
extended attention module to perceive the global features of the spatial and
spectral domains of the unsegmented whole image. By fusing local and global
features, high-precision classification of hyperspectral images is achieved.
Extensive experiments demonstrate the superiority of the proposed HS-Mamba,
outperforming state-of-the-art methods on four benchmark HSI datasets.

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