Multiscale spectral-spatial cross-extraction network for hyperspectral image classification

Convolutional neural networks (CNN) are becoming increasingly popular in modern remote sensing image classification tasks and have exhibited excellent results. For the existing CNN-based hyperspectral image (HSI) classification methods, most of which extract spatial or spectral features separately by convolution. But nearly all of these methods ignore the fact that the weighted summation of convolution may lead to appear new features in another dimension. To address this issue, a novel multiscale spectral-spatial cross-extraction network (MSSCEN) is proposed for HSI classification. Specifically, the proposed MSSCEN introduces spectral-spatial features cross extraction module (SSCEM), which fed extracted features from previous layer into spatial and spectral extraction branches separately again, so that the changes that occurred in the other domain after each convolution can be fully utilized. In addition, a new independent data augmentation module based on U-Net is designed to mitigate the problem of limited labelled samples. The paper conducts experiments on three classic hyperspectral datasets and the results demonstrate that the proposed method achieves the best classification accuracy than other state-of-the-art methods.