# Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm

arXiv: https://arxiv.org/abs/2101.03252 · HTML: https://mlsanity.com/paper/2101.03252/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)

## Built on by (most cited first, 2 of 2)

- [Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification](https://mlsanity.com/paper/2205.09250/) (2022)
- [Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation](https://mlsanity.com/paper/2210.08988/) (2022)

## Abstract (arXiv)

Supervised machine learning requires a large amount of labeled data to achieve proper test results. However, generating accurately labeled segmentation maps on remote sensing imagery, including images from synthetic aperture radar (SAR), is tedious and highly subjective. In this work, we propose to alleviate the issue of limited training data by generating synthetic SAR images with the pix2pix algorithm. This algorithm uses conditional Generative Adversarial Networks (cGANs) to generate an artificial image while preserving the structure of the input. In our case, the input is a segmentation mask, from which a corresponding synthetic SAR image is generated. We present different models, perform a comparative study and demonstrate that this approach synthesizes convincing glaciers in SAR images with promising qualitative and quantitative results.