Hyperspectral image classification based on spectral-spatial kernel principal component analysis network

Hyperspectral imagery contains both spectral information and spatial relationships among pixels. How to combine spatial information with spectral information effectively has always been a research hotspot of hyperspectral image classification. In this paper, a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) was proposed. The network is developed from the original structure of Principal Component Analysis Network. In which PCA is replaced by KPCA to extract more nonlinear features. In addition, the combination of spatial and spectral features also improves the performance of the network. At the end of the network, neighbourhood correction is added to further improve the classification accuracy. Experiments on three datasets show the effectiveness of the proposed method. Comparison with state-of-the-art deep learning-based methods indicate that the proposed method needs less training samples and has better performance.