Central-Pixel Guiding Sub-Pixel and Sub-Channel Convolution Network for Hyperspectral Image Classification

In hyperspectral image classification (HSIC), accurately extracting spatial and spectral information from hyperspectral images (HSI) is crucial for achieving precise classification. However, due to low spatial resolution and complex category boundary, mixed pixels containing features from multiple classes are inevitable in HSIs. Additionally, the spectral similarity among different classes challenge for extracting distinctive spectral features essential for HSIC. To address the impact of mixed pixels and spectral similarity for HSIC, we propose a central-pixel guiding sub-pixel and sub-channel convolution network (CP-SPSC) to extract more precise spatial and spectral features. Firstly, we designed spatial attention (CP-SPA) and spectral attention (CP-SPE) informed by the central pixel to effectively reduce spectral interference of irrelevant categories in the same patch. Furthermore, we use CP-SPA to guide 2D sub-pixel convolution (SPConv2d) to capture spatial features finer than the pixel level. Meanwhile, CP-SPE is also utilized to guide 1D sub-channel convolution (SCConv1d) in selecting more precise spectral channels. For fusing spatial and spectral information at the feature-level, the spectral feature extension transformation module (SFET) adopts mirror-padding and snake permutation to transform 1D spectral information of the center pixel into 2D spectral features. Experiments on three popular datasets demonstrate that ours outperforms several state-of-the-art methods in accuracy.