Land Cover Classification of Pi-SAR2 Polarimetric Data using Deep Learning

Abstract

The National Institute of Information and Communications Technology (NICT) has been observing the ground surface with the Pi-SAR2 airborne synthetic aperture radar, and has been studying methods to utilize the observation data effectively. Deep learning, a type of machine learning, is a method that shows high performance in the field of image classification and recognition, and that has also been actively studied in the field of remote sensing. In this paper, we report the results and verify the accuracy of a deep learning approach to land cover classification for high-resolution and full-polarimetric data observed by the Pi-SAR2.