Urban classification using PolSAR data and deep learning

The urban classification of PolSAR images is made difficult by the characteristic of a rotated target to exhibit volume scattering. In this paper we use a deep learning technique in conjunction with some statistical parameters to learn to classify urban areas irrespective of the rotation. The learning algorithm was trained to differentiate urban from non-urban areas and was able to achieve a 8.5834% validation accuracy and 6.554% test accuracy.