SpiralMamba: Spatial-Spectral Complementary Mamba With Spatial Spiral Scan for Hyperspectral Image Classification
Xu Tang, Yuexi Yao, Jingjing Ma, Xiangrong Zhang, Yuqun Yang, Bo Wang, Licheng Jiao
Abstract
Hyperspectral image (HSI) classification is crucial in the remote sensing (RS) community. In recent years, Transformers have been popular in this field due to their global information modeling capabilities. However, the quadratic complexity limits their performance under limited computational resources. Fortunately, a selective structured state space model named Mamba emerges. Like Transformer, it is good at modeling the long-distance relationships hidden in the pending data. Unlike Transformer, its complexity remains at a linear level. Therefore, a growing number of studies have been proposed to explore the usefulness of Mamba in HSI classification. Nevertheless, most of them only apply Mamba to HSIs directly but do not consider the inherent characteristics of HSIs properly. To exploit the potential of Mamba in HSI classification deeply, this paper presents a new spatial-spectral complementary Mamba with a spatial spiral scan named SpiralMamba. It mainly encloses three main components: a spatial Mamba encoder (SpaME), a spectral Mamba encoder (SpeME), and a spatial-spectral complementary fusion module (SSCFM). SpaME focuses on understanding the spatial context within HSIs. To this end, instead of the common scanning, a spatial spiral scan strategy is introduced to address the sequence transformation of non-causal HSIs. SpeME aims to comprehensively extract valuable spectral features from HSIs. To achieve this goal, besides developing a spectral bidirectional scan strategy, a multilayer convolution (MLC) is also incorporated to capture local variations within spectral tokens. SSCFM concentrates on building the complex connections between spatial and spectral features and fusing them. For this purpose, a relationship learning block (RLB) and a threshold enhancement mechanism (TEM) are developed. Positive experimental results counted on three public HSI datasets demonstrate the effectiveness of SpiralMamba. Our source codes are available at https://github.com/TangXu-Group/Hyperspectral-Images-Classification/tree/main/SpiralMamba.