Convolutional Deep Kernel Method for Land Cover Mapping from Hyperspectral Imagery

In recent years, kernel-based methods and Deep Learning (DL) models have become the two most successful Remote Sensing (RS) analysis techniques for various Earth observations, particularly hyperspectral images. However, kernel-based methods are generally considered shallow models and intrinsically inconsistent with end-to-end learning. On the other hand, end-to-end learning is one of DL models' essential features as it seems to be responsible for their proven higher performances. Nevertheless, kernel methods are based on rigid mathematical theory and can efficiently cope with high-dimensional data. This paper proposed a hybrid deep kernel model to benefit from both kernel-based methods and DL models. This novel deep kernel model, namely Convolutional Kernel Network (CKN), was applied to two benchmark hyperspectral image datasets. Moreover, the proposed hybrid method was compared to Support Vector Machine (SVM) classifiers with various kernel functions. The experimental results indicated that the CKN's outperforms SVM.