Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm

Dietrich-Sussner R, Davari A, Seehaus T, Braun M, Christlein V, Maier A, Rieß C (2021)

Publication Type: Conference contribution, Original article

Publication year: 2021

Original Authors: Rosanna Dietrich-Sussner, Amirabbas Davari, Thorsten Scehaus, Matthias Braun, Vincent Christlein, Andreas Maier, Christian Riess

Event location: Brussels

URI: https://arxiv.org/abs/2101.03252

DOI: 10.1109/IGARSS47720.2021.9553853

Abstract

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 [1]. 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.

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APA:

Dietrich-Sussner, R., Davari, A., Seehaus, T., Braun, M., Christlein, V., Maier, A., & Rieß, C. (2021). Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels, BE.

MLA:

Dietrich-Sussner, Rosanna, et al. "Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm." Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels 2021.

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