  Efficient extraction of spectral sequences and geospatial information has
always been a hot topic in hyperspectral image classification. In terms of
spectral sequence feature capture, RNN and Transformer have become mainstream
classification frameworks due to their long-range feature capture capabilities.
In terms of spatial information aggregation, CNN enhances the receptive field
to retain integrated spatial information as much as possible. However, the
spectral feature-capturing architectures exhibit low computational efficiency,
and CNNs lack the flexibility to perceive spatial contextual information. To
address these issues, this paper proposes GraphMamba--an efficient graph
structure learning vision Mamba classification framework that fully considers
HSI characteristics to achieve deep spatial-spectral information mining.
Specifically, we propose a novel hyperspectral visual GraphMamba processing
paradigm (HVGM) that preserves spatial-spectral features by constructing
spatial-spectral cubes and utilizes linear spectral encoding to enhance the
operability of subsequent tasks. The core components of GraphMamba include the
HyperMamba module for improving computational efficiency and the SpectralGCN
module for adaptive spatial context awareness. The HyperMamba mitigates clutter
interference by employing the global mask (GM) and introduces a parallel
training inference architecture to alleviate computational bottlenecks. The
SpatialGCN incorporates weighted multi-hop aggregation (WMA) spatial encoding
to focus on highly correlated spatial structural features, thus flexibly
aggregating contextual information while mitigating spatial noise interference.
Extensive experiments were conducted on three different scales of real HSI
datasets, and compared with the state-of-the-art classification frameworks,
GraphMamba achieved optimal performance.
