A neural approach under transfer learning for domain adaptation in land-cover classification using two -level cluster mapping

摘要

In this article, a domain adaptation (DA) technique using artificial neural networks based classifiers has been proposed using two-level cluster mapping technique by integrating the common data transformation and transfer learning approaches in a single framework. Here, after applying self-organizing feature mapping based clustering technique, a semi-automatic threshold selection mechanism is used to separate out most-confidently paired source-target clusters (i.e. the most similar ones) and alien target clusters (i.e. non-similar ones). Moreover, this strategy makes the proposed technique eligible to apply the transfer learning mechanism. Thereafter, the samples from the most confidently paired target clusters are transformed in terms of the corresponding source clusters using an auto-encoder. Here, the labelled samples are collected from the corresponding source clusters for the paired target clusters; whereas the transfer learning technique is used to select labelled samples from the alien target clusters. To assess the effectiveness of the proposed DA approach, experiments are conducted on the three source-target datasets and the results are compared with other state-of-the-art techniques. Results are also found to be encouraging for the proposed technique. (C) 2017 Elsevier B.V. All rights reserved.