# A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches

arXiv: https://arxiv.org/abs/2310.08705 · HTML: https://mlsanity.com/paper/2310.08705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014)
- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2003.00273/) (2020)
- [Deep Learning Meets SAR](https://mlsanity.com/paper/2006.10027/) (2020)
- [Learning Diverse Image Colorization](https://mlsanity.com/paper/1612.01958/) (2016)
- [The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion](https://mlsanity.com/paper/1807.01569/) (2018)
- [Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery](https://mlsanity.com/paper/2009.07683/) (2020)
- [PGMAN: An Unsupervised Generative Multi-adversarial Network for Pan-sharpening](https://mlsanity.com/paper/2012.09054/) (2020)
- [Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs](https://mlsanity.com/paper/2006.16644/) (2020)
- [Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening](https://mlsanity.com/paper/2109.09395/) (2021)

## Built on by (most cited first, 1 of 1)

- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)

## Abstract (arXiv)

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.