This paper presents a framework for a semisupervised domain adaptation method for remote sensingimage classification. Most of the representation-based domain adaptation methods attempt to find a total transformation matrix for all the samples from the source domain; however, they ignore the individual changes in each class, which often leads to the misalignment of the samples in each class between the two domains. This paper attempts to find new representations for the samples in different classes from the source domain by multiple linear transformations, which corresponds to the practical changes in each class to a higher degree.
Furthermore, to avoid the influence of outliers and noise in the source domain samples, low-rank reconstruction is further applied to make the domain adaptation method more robust. In addition, in the stage of predicting the unlabeled samples by label propagation (LP), the proposed LP with instance weighting can effectively further reduce the negative effect of misleading samples from the source domain. The results obtained with a QuickBird data set and a hyperspectral data set confirm the effectiveness and reliability of the proposed method.