Spectral Selection Convolution and Overlapped-Centering Mamba Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a critical task in remote sensing. Recently, Deep Learning (DL) methods, particularly State Space Models (SSMs) like Mamba, have garnered significant attention due to their linear computational complexity in handling long sequences. However, processing the rich yet redundant spectral bands in HSIs remains challenging for these approaches, often resulting in models with excessive parameters. Moreover, existing DL-based methods frequently overlook explicit cross-channel dependencies. This limitation hinders their ability to effectively aggregate relevant information from redundant spectral bands. Additionally, the heavy reliance of DL-based models on extensive training data conflicts with the scarcity of labeled samples in HSI scenarios. This necessitates the incorporation of prior knowledge to mitigate data limitations. To address these challenges, we propose SliMamba, a lightweight Convolution-Mamba architecture for HSI classification. SliMamba introduces two key components: Spectral Selection Convolution (SSC) and Overlapped-Centering Mamba (OC-Mamba). SSC swaps dimensions between spectral and spatial axes while preserving the original spectral information. It then utilizes a small set of convolutional kernels to learn spectral weights within the receptive field, thereby achieving cross-channel feature representation. OC-Mamba integrates prior knowledge of the center pixel with local feature enhancement. It combines one-pixel Overlapped SSM and Centering SSM to extract global-local and spectral-spatial features from HSI feature maps. Extensive evaluations on four benchmark datasets demonstrate that SliMamba consistently outperforms state-of-the-art methods in classification accuracy while significantly reducing model complexity. The source code is available at https://github.com/flyzzie/SliMamba.