Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm
This work provides a method to generate synthetic training data for machine learning models, which is beneficial for researchers and practitioners working with remote sensing and SAR imagery where data labeling is labor-intensive and subjective.
This paper addresses the scarcity of labeled SAR imagery for machine learning by generating synthetic SAR images of glaciers from arbitrary segmentation masks using the pix2pix algorithm. The approach successfully synthesizes convincing glacier SAR images, demonstrating promising qualitative and quantitative results.
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.