Input-output-consistent domain adaptation algorithm for remote sensing data classification

A domain adaptation problem is dealt with where the marginal probability in a target domain is different from but correlated to the one in the source domain but the classification tasks are the same. This problem occurs frequently in classification of remote sensing data, e.g., when data are collected in the same area but at different dates or when data are acquired by the same sensor with the same class label set but in different locations. Traditional learning machines cannot deal with this problem in a satisfactory manner. In this paper, we propose a rationale input-output-consistency where samples in the same cluster and defined by spectral signatures (input space) should have the same class label (output space) if they are accurately classified. With the rationale, samples of high confidence in the target domain are selected to define a new prediction function. Since two domains that are related can have different distributions, the data in the source domain which cannot adapt to the distribution in the target domain are deleted from the training data set. Therefore, the proposed algorithm is denoted as input-consistent-output domain adaptation (iCODA) and works in an iterative way. After the selection of highly-confident target samples and the deletion of source data, a new training data set is used to define a new prediction model. The proposed iCODA algorithm was evaluated on EO-1 hyperspectral data sets from Botswana. Experimental results demonstrate much better classification accuracies when compared to a traditionally used supervised classifier.