DBMLLA: Double-Branch Mamba-Like Linear Attention Network for Hyperspectral Image Classification

Lianhui Liang, Peiyi Xie, Ying Zhang, Jiaxin Li, Zhe Zhang, Jun Li, Antonio Plaza

Abstract: Convolutional neural networks (CNNs) and transformers have made remarkable achievements in hyperspectral image classification (HSIC). Unfortunately, CNN-based methods struggle to capture the contextual dependencies between pixels in HSIs, while transformer-based methods suffer from quadratic computational complexity. Recently, the Mamba model has shown great potential as it can describe long-range dependencies between HSI pixels with linear computational complexity. Yet, Mamba still faces significant challenges in terms of global modeling. Inspired by the Mamba model framework and transformers, a novel double-branch Mamba-like linear attention (DBMLLA) network is proposed for HSIC, achieving efficient global dependency modeling. Specifically, the proposed DBMLLA combines an embedding module, a spatial–spectral Mamba-like linear attention (SS-MLLA) module, and a fusion module. In the embedding module, an absolute position embedding module is introduced for better extraction of global features. In the SS-MLLA module, we design the spatial MLLA (SpaMLLA) block and the spectral MLLA (SpeMLLA) block to exploit the spatial and spectral information of the HSI. In addition, SS-MLLA is improved using depthwise separable convolution (DSC) to enhance the model’s ability to extract deeper local feature information. Through experiments conducted on four public hyperspectral datasets, it is demonstrated that the proposed model consistently outperforms state-of-the-art approaches.

External IDs:dblp:journals/tgrs/LiangXZLZLP25