Cascaded dual-scale crossover network for hyperspectral image classification

摘要

In recent years, deep neural networks have exhibited numerous advantages in hyperspectral image classification (HIC). However, owing to the limited number of training samples of hyperspectral images (HSIs), the network structure should not be designed too deep to retard the overfitting phenomenon. This study proposes a cascaded dual-scale crossover network for HIC, which not only could extract rich features, but also does not make the network deeper. It continuously connects two different cascaded dual-scale crossover blocks, and automatically extracts the spectral–spatial features of HSIs. Moreover, for the limited training samples, the proposed network could flexibly capture more discriminant contextual features by using different spectral-size and spatial-size convolution kernels. Furthermore, two different cross-merge methods are designed to improve the information flow and contrast of the images to obtain parts of interest for the images. Two skip structures are also used for alleviating overfitting and accelerating the network training. Additional experimental results on some datasets, including Indian Pines, Kennedy Space Center, and University of Pavia, verify the feasibility of the proposed network. Namely, the classification accuracy of the proposed network is superior to that of other existing networks.