Composite kernel learning network for hyperspectral image classification

ABSTRACT The small sample problem has always been a serious challenge in hyperspectral image (HSI) classification. In order to obtain satisfactory results when the training samples are insufficient, the information around the training samples should be fully utilized. In this paper, we focus on small sample learning and propose a novel composite kernel learning network (CKLNet) for HSI classification. First, principal component analysis and extended morphological analysis are utilized to extract features. Then, we introduce generalized kernel method into deep learning technology. The spatial-spectral composite kernel learning (SSCKL) module is developed to construct discriminative and robust spatial-spectral generalized kernel features. In the process of constructing kernel features, the deep correlation information between samples is extracted simultaneously. The kernel hyperparameters in SSCKL are automatically learnt through backpropagation, thus avoiding the need to spend a lot of time on cross-validation. Finally, inspired by U-Net, a global-local feature extraction (GLFE) module is designed to extract spatial features of different scales. A set of classification probability maps can be obtained by the 1 1 convolutional layer in the GLFE module. Experimental results on three widely used datasets demonstrate the effectiveness of the proposed CKLNet.