Land cover analysis using hyperspectral images (HSI) remains an open problem
due to their low spatial resolution and complex spectral information. Recent
studies are primarily dedicated to designing Transformer-based architectures
for spatial-spectral long-range dependencies modeling, which is computationally
expensive with quadratic complexity. Selective structured state space model
(Mamba), which is efficient for modeling long-range dependencies with linear
complexity, has recently shown promising progress. However, its potential in
hyperspectral image processing that requires handling numerous spectral bands
has not yet been explored. In this paper, we innovatively propose S$^2$Mamba, a
spatial-spectral state space model for hyperspectral image classification, to
excavate spatial-spectral contextual features, resulting in more efficient and
accurate land cover analysis. In S$^2$Mamba, two selective structured state
space models through different dimensions are designed for feature extraction,
one for spatial, and the other for spectral, along with a spatial-spectral
mixture gate for optimal fusion. More specifically, S$^2$Mamba first captures
spatial contextual relations by interacting each pixel with its adjacent
through a Patch Cross Scanning module and then explores semantic information
from continuous spectral bands through a Bi-directional Spectral Scanning
module. Considering the distinct expertise of the two attributes in homogenous
and complicated texture scenes, we realize the Spatial-spectral Mixture Gate by
a group of learnable matrices, allowing for the adaptive incorporation of
representations learned across different dimensions. Extensive experiments
conducted on HSI classification benchmarks demonstrate the superiority and
prospect of S$^2$Mamba. The code will be available at:
this https URL

通过创新性地提出 S^2Mamba，即一种适用于高光谱图像分类的空间 - 光谱状态空间模型，以发掘空间 - 光谱上下文特征，实现更高效准确的土地覆盖分析。