S$^{2}$DCN: Spectral–Spatial Difference Convolution Network for Hyperspectral Image Classification

A novel spectral–spatial difference convolution network (S
$^{2}$
DCN) is proposed for hyperspectral image (HSI) classification, which integrates the difference principle into the deep learning framework. S
$^{2}$
DCN employs a learnable gradient encoding pattern to extract important detail features in spectral and spatial domains, alleviating the information loss caused by the oversmoothing effect in deep feature extraction. Specifically, the feature extraction modules in S
$^{2}$
DCN are designed, namely spectral difference convolution (SeDC) module and spatial difference convolution (SaDC) module. The SeDC module performs 1-D difference convolution in the spectral domain to capture peak-valley information in sensitive narrow bands, enhance subtle spectral differences, and preserve fine-grained features. The SaDC module employs 2-D difference convolution in the spatial domain, integrating fine-structural features while preserving the deep abstract features extracted by vanilla convolutions. This further empowers the capability of the model to extract discriminative features. A series of experiments are performed on four publicly available HSI datasets to demonstrate the effectiveness of S
$^{2}$
DCN method, which is compared with current state-of-the-art models. The experimental results show that the proposed S
$^{2}$
DCN outperforms competitors and achieves optimal classification performance.