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).

翻译：对复杂光谱-空间关系建模的有效性和效率对于高光谱图像分类都至关重要。大多数现有的基于CNN和Transformer的方法仍然存在计算负担重的问题，并且在捕获全局-局部光谱-空间特征表示方面仍有改进空间。为此，我们提出了一种新颖的轻量级并行设计，称为轻量级双流Mamba-卷积网络，用于HSI分类。具体而言，首先开发了并行的轻量级Mamba和CNN块来提取全局和局部光谱-空间特征。首先，提出了交叉注意力光谱-空间Mamba模块，以在线性复杂度下利用Mamba的全局建模能力。在该模块中，设计了动态位置嵌入以增强视觉序列的空间位置信息。轻量级光谱/空间Mamba块包含高效的扫描策略和轻量级Mamba设计，以高效提取全局光谱-空间特征。并且设计了交叉注意力光谱-空间融合来学习互相关性并融合光谱-空间特征。其次，提出了轻量级光谱-空间残差卷积模块，其具有轻量级光谱和空间分支，通过残差学习提取局部光谱-空间特征。最后，提出了自适应全局-局部融合，以动态结合全局Mamba特征和局部卷积特征，形成全局-局部光谱-空间表示。与最先进的HSI分类方法相比，实验结果表明，DualMamba在三个公共HSI数据集上实现了显著的分类精度，并且在模型参数和浮点运算次数方面实现了卓越的降低。