A Benchmarking Protocol for SAR Colorization: From Regression to Deep
  Learning Approaches
Kangqing Shen Gemine Vivone Xiaoyuan Yang Simone Lolli Michael Schmitt
Synthetic aperture radar (SAR) images are widely used in remote sensing.
Interpreting SAR images can be challenging due to their intrinsic speckle noise
and grayscale nature. To address this issue, SAR colorization has emerged as a
research direction to colorize gray scale SAR images while preserving the
original spatial information and radiometric information. However, this
research field is still in its early stages, and many limitations can be
highlighted. In this paper, we propose a full research line for supervised
learning-based approaches to SAR colorization. Our approach includes a protocol
for generating synthetic color SAR images, several baselines, and an effective
method based on the conditional generative adversarial network (cGAN) for SAR
colorization. We also propose numerical assessment metrics for the problem at
hand. To our knowledge, this is the first attempt to propose a research line
for SAR colorization that includes a protocol, a benchmark, and a complete
performance evaluation. Our extensive tests demonstrate the effectiveness of
our proposed cGAN-based network for SAR colorization. The code will be made
publicly available.