Unsupervised domain adaptation in land-cover classification under neural approach using feature-level ensemble

In this manuscript, a feature level ensemble based technique has been proposed using artificial neural networks (ANNs) for an unsupervised domain adaptation (DA) in land-cover classification. Here, ANNs in form of an auto-encoder (AE) and a restricted Boltzman machine (RBM) have been used to learn the sample distribution of both source and target domains together during training. The trained ANNs are then used to find new features suitable for adaptive learning across the two domains having different sample distributions. Finally, the new features obtained from AE and RBM are fused to obtain the final train and test set. Experimentation has been carried out using patterns collected from Landsat-8 multi-spectral satellite images captured over various regions of India. The prediction accuracies of target classes obtained for the proposed scheme show encouraging results when compared with that of other state-of-the-art DA techniques.