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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.