  We propose a novel approach for generating high quality visible-like images
from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative
Adversarial Network (GAN) architectures. The proposed approach is based on a
cascaded network of convolutional neural nets (CNNs) for despeckling and image
colorization. The cascaded structure results in faster convergence during
training and produces high quality visible images from the corresponding SAR
images. Experimental results on both simulated and real SAR images show that
the proposed method can produce visible-like images better compared to the
recent state-of-the-art deep learning-based methods.
