Domain adaptation for land-cover classification of remotely sensed images using ensemble of Multilayer Perceptrons

Abstract

Domain Adaptation (DA) for remotely sensed images using ensemble of Multilayer Perceptrons (MLP) is presented in this article. Using the labelled information from a source domain, the proposed method utilises the disagreement among the MLPs to figure out the `most informative' patterns from the target domain using transfer learning. These selected patterns are then labelled by human expert and are used for training the ensemble. Finally the land-cover classes for a target region are predicted by applying a non-trainable majority voting rule among different MLPs in ensemble. Experiments have been conducted on multispectral images for two different regions in India obtained from Landsat-8 satellite and the proposed DA method outperforms the corresponding random sampling approach.