The Added Value of Cycle-GAN for Agriculture Studies

Abstract

It is significant to monitor the phenological stages of agricultural crops with accurate and up-to-date information. In monitoring the phenological phases of some crops, optical remote sensing data offers significant spectral information and outstanding feature identification. However, a continuous time series of optical remote sensing data is difficult to obtain due to the weather dependency of optical acquisitions. In this paper, the feasibility of transfer learning between the features of Sentinel-1 and Sentinel-2 is evaluated to reduce these difficulties. A feature translation based on deep learning (DL) method, namely Cycle-Consistent Generative Adversarial Networks (cycle-GAN), was applied between Sentinel-1 and Sentinel-2 data. In order to evaluate the effect of the cycle-GAN method on crop type mapping and identification, Random Forest classification was applied to four different cases (Real SAR, Fake Optical + Real SAR, Real Optical, and Real Optical + Real SAR).