CFRWD-GAN for SAR-to-Optical Image Translation
Synthetic aperture radar (SAR) images have been extensively used in earthquake monitoring, resource survey, agricultural forecasting, etc. However, it is a challenge to interpret SAR images with severe speckle noise and geometric deformation due to the nature of radar imaging. The translation of SAR...
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/10/2547 |
| _version_ | 1797598464401473536 |
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| author | Juan Wei Huanxin Zou Li Sun Xu Cao Shitian He Shuo Liu Yuqing Zhang |
| author_facet | Juan Wei Huanxin Zou Li Sun Xu Cao Shitian He Shuo Liu Yuqing Zhang |
| author_sort | Juan Wei |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) images have been extensively used in earthquake monitoring, resource survey, agricultural forecasting, etc. However, it is a challenge to interpret SAR images with severe speckle noise and geometric deformation due to the nature of radar imaging. The translation of SAR-to-optical images provides new support for the interpretation of SAR images. Most of the existing translation networks, which are based on generative adversarial networks (GANs), are vulnerable to part information loss during the feature reasoning stage, making the outline of the translated images blurred and semantic information missing. Aiming to solve these problems, cross-fusion reasoning and wavelet decomposition GAN (CFRWD-GAN) is proposed to preserve structural details and enhance high-frequency band information. Specifically, the cross-fusion reasoning (CFR) structure is proposed to preserve high-resolution, detailed features and low-resolution semantic features in the whole process of feature reasoning. Moreover, the discrete wavelet decomposition (WD) method is adopted to handle the speckle noise in SAR images and achieve the translation of high-frequency components. Finally, the WD branch is integrated with the CFR branch through an adaptive parameter learning method to translate SAR images to optical ones. Extensive experiments conducted on two publicly available datasets, QXS-SAROPT and SEN1-2, demonstrate a better translation performance of the proposed CFRWD-GAN compared to five other state-of-the-art models. |
| first_indexed | 2024-03-11T03:21:31Z |
| format | Article |
| id | doaj.art-7fe61d9ac2144ff48c5238b7a89e1d5d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:31Z |
| publishDate | 2023-05-01 |
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| series | Remote Sensing |
| spelling | doaj.art-7fe61d9ac2144ff48c5238b7a89e1d5d2023-11-18T03:06:41ZengMDPI AGRemote Sensing2072-42922023-05-011510254710.3390/rs15102547CFRWD-GAN for SAR-to-Optical Image TranslationJuan Wei0Huanxin Zou1Li Sun2Xu Cao3Shitian He4Shuo Liu5Yuqing Zhang6College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaSynthetic aperture radar (SAR) images have been extensively used in earthquake monitoring, resource survey, agricultural forecasting, etc. However, it is a challenge to interpret SAR images with severe speckle noise and geometric deformation due to the nature of radar imaging. The translation of SAR-to-optical images provides new support for the interpretation of SAR images. Most of the existing translation networks, which are based on generative adversarial networks (GANs), are vulnerable to part information loss during the feature reasoning stage, making the outline of the translated images blurred and semantic information missing. Aiming to solve these problems, cross-fusion reasoning and wavelet decomposition GAN (CFRWD-GAN) is proposed to preserve structural details and enhance high-frequency band information. Specifically, the cross-fusion reasoning (CFR) structure is proposed to preserve high-resolution, detailed features and low-resolution semantic features in the whole process of feature reasoning. Moreover, the discrete wavelet decomposition (WD) method is adopted to handle the speckle noise in SAR images and achieve the translation of high-frequency components. Finally, the WD branch is integrated with the CFR branch through an adaptive parameter learning method to translate SAR images to optical ones. Extensive experiments conducted on two publicly available datasets, QXS-SAROPT and SEN1-2, demonstrate a better translation performance of the proposed CFRWD-GAN compared to five other state-of-the-art models.https://www.mdpi.com/2072-4292/15/10/2547SAR-to-optical image translationgenerative adversarial networkscross-fusion reasoning structurewavelet decomposition |
| spellingShingle | Juan Wei Huanxin Zou Li Sun Xu Cao Shitian He Shuo Liu Yuqing Zhang CFRWD-GAN for SAR-to-Optical Image Translation Remote Sensing SAR-to-optical image translation generative adversarial networks cross-fusion reasoning structure wavelet decomposition |
| title | CFRWD-GAN for SAR-to-Optical Image Translation |
| title_full | CFRWD-GAN for SAR-to-Optical Image Translation |
| title_fullStr | CFRWD-GAN for SAR-to-Optical Image Translation |
| title_full_unstemmed | CFRWD-GAN for SAR-to-Optical Image Translation |
| title_short | CFRWD-GAN for SAR-to-Optical Image Translation |
| title_sort | cfrwd gan for sar to optical image translation |
| topic | SAR-to-optical image translation generative adversarial networks cross-fusion reasoning structure wavelet decomposition |
| url | https://www.mdpi.com/2072-4292/15/10/2547 |
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