Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs

Synthetic aperture radar (SAR) provides an all-weather and all-time imaging platform, which is more reliable than electro-optical (EO) remote sensing imagery under extreme weather/lighting conditions. While many large-scale EO-based remote sensing datasets have been released for computer vision task...

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Main Authors: Qing, Yuanyuan, Zhu, Jiang, Feng, Hongchuan, Liu, Weixian, Wen, Bihan
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/169236
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1692362023-07-14T15:39:27Z Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs Qing, Yuanyuan Zhu, Jiang Feng, Hongchuan Liu, Weixian Wen, Bihan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Image Translation Generative Adversarial Networks Synthetic aperture radar (SAR) provides an all-weather and all-time imaging platform, which is more reliable than electro-optical (EO) remote sensing imagery under extreme weather/lighting conditions. While many large-scale EO-based remote sensing datasets have been released for computer vision tasks, there are few publicly available SAR image datasets due to the high costs associated with acquisition and labeling. Recent works have applied deep learning methods for image translation between SAR and EO. However, the effectiveness of those techniques on high-resolution images has been hindered by a common limitation. Non-linear geometric distortions, induced by different imaging principles of optical and radar sensors, have caused insufficient pixel-wise correspondence between an EO-SAR patch pair. Such a phenomenon is not prominent in low-resolution EO-SAR datasets, e.g., SEN1-2, one of the most frequently used datasets, and thus has been seldom discussed. To address this issue, a new dataset SN6-SAROPT with sub-meter resolution is introduced, and a novel image translation algorithm designed to tackle geometric distortions adaptively is proposed in this paper. Extensive experiments have been conducted to evaluate the proposed algorithm, and the results have validated its superiority over other methods for both SAR to EO (S2E) and EO to SAR (E2S) tasks, especially for urban areas in high-resolution images. Published version 2023-07-10T02:55:41Z 2023-07-10T02:55:41Z 2023 Journal Article Qing, Y., Zhu, J., Feng, H., Liu, W. & Wen, B. (2023). Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs. Remote Sensing, 15(7), 1878-. https://dx.doi.org/10.3390/rs15071878 2072-4292 https://hdl.handle.net/10356/169236 10.3390/rs15071878 2-s2.0-85152561192 7 15 1878 en Remote Sensing © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Image Translation
Generative Adversarial Networks
spellingShingle Engineering::Electrical and electronic engineering
Image Translation
Generative Adversarial Networks
Qing, Yuanyuan
Zhu, Jiang
Feng, Hongchuan
Liu, Weixian
Wen, Bihan
Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs
description Synthetic aperture radar (SAR) provides an all-weather and all-time imaging platform, which is more reliable than electro-optical (EO) remote sensing imagery under extreme weather/lighting conditions. While many large-scale EO-based remote sensing datasets have been released for computer vision tasks, there are few publicly available SAR image datasets due to the high costs associated with acquisition and labeling. Recent works have applied deep learning methods for image translation between SAR and EO. However, the effectiveness of those techniques on high-resolution images has been hindered by a common limitation. Non-linear geometric distortions, induced by different imaging principles of optical and radar sensors, have caused insufficient pixel-wise correspondence between an EO-SAR patch pair. Such a phenomenon is not prominent in low-resolution EO-SAR datasets, e.g., SEN1-2, one of the most frequently used datasets, and thus has been seldom discussed. To address this issue, a new dataset SN6-SAROPT with sub-meter resolution is introduced, and a novel image translation algorithm designed to tackle geometric distortions adaptively is proposed in this paper. Extensive experiments have been conducted to evaluate the proposed algorithm, and the results have validated its superiority over other methods for both SAR to EO (S2E) and EO to SAR (E2S) tasks, especially for urban areas in high-resolution images.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Qing, Yuanyuan
Zhu, Jiang
Feng, Hongchuan
Liu, Weixian
Wen, Bihan
format Article
author Qing, Yuanyuan
Zhu, Jiang
Feng, Hongchuan
Liu, Weixian
Wen, Bihan
author_sort Qing, Yuanyuan
title Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs
title_short Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs
title_full Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs
title_fullStr Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs
title_full_unstemmed Two-way generation of high-resolution EO and SAR images via dual distortion-adaptive GANs
title_sort two-way generation of high-resolution eo and sar images via dual distortion-adaptive gans
publishDate 2023
url https://hdl.handle.net/10356/169236
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