A multi-level weighted transformation based neuro-fuzzy domain adaptation technique using stacked auto-encoder for land-cover classification

In this manuscript, a neuro-fuzzy domain adaptation (DA) technique has been proposed for a multi-level incremental transformation of the source-target features to find an intermediate space with lesser cross-domain distribution difference at each level. In the present investigation, the unsupervised layers of a stacked auto-encoder are used for granular transformation of the weighted samples (or group of samples) at every level. Out of the three, the first two layers of the stack involve unsupervised weighted transformation of source-target samples without using any labelled information from the target domain. After that, a fuzzy membership-based transfer learning scheme has been used to capture the target-distinctive information thereby facilitating a selective transformation between matching source-target sample groups in the third level. Finally, more accurate class-label predictions for the unknown target samples are obtained using the labelled source samples in the transformed (source-target) feature space. To validate the effectiveness of the proposed approach, experimentation has been carried out using samples collected from various multi-spectral satellite images captured over various source and target regions of India. The attained results show superior performance in target class prediction for the proposed DA scheme when compared to other state-of-the-art DA techniques for land-cover classification.