Exploiting GAN-Based SAR to Optical Image Transcoding for Improved Classification via Deep Learning

Abstract:
Learning the proxy task of transcoding SAR images into optical images forces an employed conditional generative adversarial network (GAN) to distinguish between different land surfaces. Such a network can then be used to build a classifier with significantly fewer free parameters that generalizes well even when trained on a very small amount of labeled data. We train such a GAN on aligned Sentinel-1 and Sentinel-2 image pairs. We then show that a pre-trained classifier using these features learned from transcoding outperforms classifiers that are trained from scratch when only a very limited amount of labeled pixels are available for training.