EDCGAN: Encoder Decoder based Conditional GAN for SAR to Optical image translation ✱
Avinash Chouhan, Nitesh Jindal, Arijit Sur, Dibyajyoti Chutia, Shiv Prasad Aggarwal · 2022
High-resolution optical images are heavily utilized in various remote sensing applications. The optical images cannot reflect the actual ground information in cloudy conditions. SAR images are used to solve this for their ability to see through clouds. But SAR images are usually available with coarser resolutions. So, there is a need to produce an optical image from a SAR image to overcome bad weather and poor resolution in a single go. In this paper, a novel deep learning architecture named EDCGAN is proposed. The proposed architecture is an encoder-decoder-based conditional GAN that uses multi-scale attentive discrimination to get accurate SAR to RGB image translation. In addition, we have used residual connections, spatial & channel-wise attention for better feature representation. A set of extensive experimentations show that this architecture outperforms the existing state-of-the-art method in terms of PSNR, SSIM, and FSIM_c values for the WHU-SENCity dataset.