A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches
This work addresses the problem of interpreting grayscale SAR images for remote sensing applications, but it is incremental as it builds on existing colorization techniques in a specific domain.
The paper tackles the challenge of colorizing synthetic aperture radar (SAR) images by proposing a supervised learning-based research line, including a protocol for generating synthetic color SAR images, baselines, and a cGAN-based method, with extensive tests demonstrating its effectiveness.
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.