The effectiveness and efficiency of modeling complex spectral-spatial
relations are both crucial for Hyperspectral image (HSI) classification. Most
existing methods based on CNNs and transformers still suffer from heavy
computational burdens and have room for improvement in capturing the
global-local spectral-spatial feature representation. To this end, we propose a
novel lightweight parallel design called lightweight dual-stream
Mamba-convolution network (DualMamba) for HSI classification. Specifically, a
parallel lightweight Mamba and CNN block are first developed to extract global
and local spectral-spatial features. First, the cross-attention
spectral-spatial Mamba module is proposed to leverage the global modeling of
Mamba at linear complexity. Within this module, dynamic positional embedding is
designed to enhance the spatial location information of visual sequences. The
lightweight spectral/spatial Mamba blocks comprise an efficient scanning
strategy and a lightweight Mamba design to efficiently extract global
spectral-spatial features. And the cross-attention spectral-spatial fusion is
designed to learn cross-correlation and fuse spectral-spatial features. Second,
the lightweight spectral-spatial residual convolution module is proposed with
lightweight spectral and spatial branches to extract local spectral-spatial
features through residual learning. Finally, the adaptive global-local fusion
is proposed to dynamically combine global Mamba features and local convolution
features for a global-local spectral-spatial representation. Compared with
state-of-the-art HSI classification methods, experimental results demonstrate
that DualMamba achieves significant classification accuracy on three public HSI
datasets and a superior reduction in model parameters and floating point
operations (FLOPs).

提出了一种轻量级双流 Mamba 卷积网络 (DualMamba) 用于高光谱图像分类，通过全局局部光谱空间特征提取和全局局部融合实现了显著的分类准确度和模型参数减少。