Class-Specific Dictionary Based Semi-Supervised Domain Adaptation for Land-Cover Classification of Aerial Images

Abstract

Supervised scene classification of aerial images plays an important role in land-cover classification. However, it is difficult and time-consuming to annotate the required number of samples. Moreover, the conventional classifiers cannot produce satisfactory results without sufficient labelled data. Semi-supervised domain adaptation methods can overcome this problem to some extent by transferring previously labelled data. But the feature distribution bias caused by different sensors, seasons or locations may lead to a lower performance. In order to reduce the feature distribution bias and keep the discriminative ability, we propose a novel class-specific dictionary-based semi-supervised domain adaptation (CDSDA) framework when newly labelled data are unavailable. The CDSDA first learns a discriminative class-specific dictionary in the source domain. Then the target dictionary are obtained by designing an objective function in an iterative process in order to reduce the feature distribution bias and keep the discriminative ability. The target and source features are both mapped to the new feature space by the learnt target dictionary for classification. The CDSDA method was tested on a large aerial image where two benchmark datasets serve as the training dataset. The experiments on a larger aerial image demonstrate that the CDSDA method performs better than some previous domain adaptation methods in the case of no target labelled data.