Graph Embedding and Distribution Alignment for Domain Adaptation in Hyperspectral Image Classification
Yi Huang, Jiangtao Peng, Yujie Ning, Weiwei Sun, Qian Du
Abstract
Recent studies in cross-domain classification have shown that discriminant information of both source and target domains is very important. In this article, we propose a new domain adaptation (DA) method for hyperspectral image (HSI) classification, called graph embedding and distribution alignment (GEDA). GEDA uses the graph embedding method and a pseudo-label learning method to learn interclass and intraclass divergence matrices of source and target domains, which preserves the local discriminant information of both domains. Meanwhile, spatial and spectral features of HSI are used, and distribution alignment and subspace alignment are performed to minimize the spectral differences between domains. We perform DA tasks on Yancheng, Botswana, University of Pavia, and Center of Pavia, Shanghai and Hangzhou data sets. Experimental results show that the classification performance of the proposed GEDA is better than that of existing DA methods.