Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification

Abstract

This article proposes a novel unsupervised domain adaptation (DA) method called ideal regularized discriminative multiple kernel subspace alignment (IRDMKSA) for hyperspectral image (HSI) classification. The proposed IRDMKSA method includes three main steps: ideal regularization, discriminative multiple kernel learning, and subspace alignment. The ideal regularization strategy exploits label information of source domain to refine the standard source and target kernels and also to build a connection between them. The discriminative multiple kernel learning can learn a composite kernel to describe the nonlinearity of HSI samples by fusing complementary information among different single kernels. Finally, the subspace alignment is used to diminish the difference between source and target composite kernels. The proposed IRDMKSA method exploits both the sample similarity and label similarity and makes the resulting kernel more appropriate for DA tasks. Experimental results on four DA tasks show that the performance of IRDMKSA is better than some classical unsupervised DA methods for the HSI classification.