A New Convolutional Kernel Classifier for Hyperspectral Image Classification

Multiplekernel learning (MKL) algorithms are among the most successful classification methods for hyperspectral data. Nevertheless, these algorithms suffer from two main drawbacks of computational complexity and debility to admit to the end-to-end learning paradigm. This article proposed a convolutional kernel classifier (CKC) for hyperspectral remote sensing images to address these issues. The CKC uses the Nystrom approximation method to estimate a low-rank approximation of the basis kernels, thus solves the issues associated with the high dimensionality of the basis kernels. The CKC uses deep architecture to learn the optimal combination of the basis kernels and the classification task to enable end-to-end learning. The proposed CKC's architecture is based on a one-dimensional-convolutional neural network (CNN-1-D), and it uses kernel dropout to prevent overfitting. It is the first instance of deep-kernel algorithms in the field of remote sensing. The proposed method was compared with several well-known hyperspectral image analysis MKL algorithms, including a multi-kernel variant of the deep kernel machine optimization, MKL-average, Simple-MKL, and generalize MKL, and state-of-the-art deep learning models, including Vanilla recurrent neural network (VanillaRNN) and CNN-1-D in classifying four benchmark hyperspectral datasets. The experimental results show that the CKC consistently outperforms all the competitor methods, and its runtime is lower than its MKL algorithm counterparts on four benchmark hyperspectral datasets. Moreover, the Nystrom approximation solves the high dimensionality of the basis kernels and boosts classification accuracy. The source codes of CKC are available from: https://github.com/MohsenAnsari1373/A-New-Convolutional-Kernel-Classifier-for-Hyperspectral-Image-Classification.