Deep Transfer Learning For Remote Sensing Ii

This Special Issue is dedicated to exploring the potential of deep transfer learning in remote sensing (RS) image processing. Transfer learning attempts to reduce the high demand for labeled data on a target task by reusing knowledge obtained from one or more source tasks. Deep transfer learning, which can overcome the semantic gap between different datasets, has become a research frontier and can utilize the information contained in existing labeled data to help make predictions for newly collected RS data. Topics of interest include, but are not limited to: machine learning, computer vision, artificial intelligence, and more.