Classifier-Constrained Deep Adversarial Domain Adaptation for Cross-Domain Semisupervised Classification in Remote Sensing Images

Abstract

This letter presents a classifier-constrained deep adversarial domain adaptation (CDADA) method for cross-domain semisupervised classification in remote sensing (RS) images. A deep convolutional neural network (DCNN) is used to build feature representations to describe the semantic content of scenes before the adaptation process. Then, adversarial domain adaptation is used to align the feature distribution of the source and the target. Specifically, two different land-cover classifiers are used as a discriminator to consider land-cover decision boundaries between classes and increase their distance to separate them from the original land-cover class boundaries. The generator then creates robust transferable features far from the original land-cover class boundaries under the classifier constraint. The experimental results of six scenarios built from three benchmark RS scene data sets (AID, Merced, and RSI-CB data sets) are reported and discussed.