Semisupervised Two-Level Fusion-Based Autoencoded Approach for Low-Cost Domain Adaptation of Remotely Sensed Images

In this letter, a low-cost semisupervised domain adaptation technique has been proposed using a two-level fusion of artificial neural networks. The proposed technique has been aimed to solve the problem of sample selection bias using a one-time collection of a few patterns from the target domain under semisupervised framework. To assess the effectiveness, experiments are conducted on the two source-target data sets acquired over India. The results are found to be encouraging.