Correlation Alignment Based On Sparse Matrix Transform For Unsupervised Domain Adaptation In Hyperspectral Image Classification

This paper proposes an unsupervised domain adaptation (DA) method called correlation alignment based on sparse matrix transform (CORAL-SMT) for hyperspectral image (HSI) classification. In CORAL-SMT, the covariance of source and target domain are constrained to have an eigen-decomposition that can be represented as a sparse matrix transform. Under maximum likelihood framework, based on greedy minimization strategy, the covariances can be efficiently estimated and are always positive definite. The proposed method is compared with some classical unsupervised domain adaptation methods. Experimental results on the City of Pavia hyperspectral data set demonstrate the effectiveness of CORAL-SMT.