Deep Neural Network-Based Heterogeneous Domain Adaptation Using Ensemble Decision Making in Land Cover Classification

A domain adaptation (DA) problem is investigated for land cover classification by utilizing the ensemble decision approach of deep neural networks to address the extra and missing class problem. Two different pretrained models followed by two autoencoders are used to extract the two sets of cross-domain features for samples in the reduced shape. Thereafter, the primary labels and probability scores of target samples are obtained using two different classifiers trained over the source samples with these different features. Moreover, unlike other open set DA techniques, the proposed framework is capable of identifying extra classes separately by exploring the agreement between these two classifiers. Experiments were carried out using three aerial image datasets, and the results are found to be encouraging for the proposed scheme in comparison with other state-of-the-art techniques.