DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jaehyup | ko |
dc.contributor.author | Kim, Hyun-Ho | ko |
dc.contributor.author | Seo, Doochun | ko |
dc.contributor.author | Kim, Munchurl | ko |
dc.date.accessioned | 2024-01-16T09:01:58Z | - |
dc.date.available | 2024-01-16T09:01:58Z | - |
dc.date.created | 2024-01-16 | - |
dc.date.created | 2024-01-16 | - |
dc.date.issued | 2024-01 | - |
dc.identifier.citation | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v.21, pp.1 - 5 | - |
dc.identifier.issn | 1545-598X | - |
dc.identifier.uri | http://hdl.handle.net/10203/317877 | - |
dc.description.abstract | Recently, the analysis and use of synthetic aperture radar (SAR) imagery have become crucial for surveillance, military operations, and environmental monitoring. A common challenge with SAR images is the presence of speckle noise, which can hinder their interpretability. To enhance the clarity of SAR images, this letter introduces a novel SAR-to-electro-optical (EO) image translation (SET) network, called SGCL-SET, which first incorporates EO object label information for stable translation. We use a pretrained segmentation network to provide the segmentation regions with their labels into learning the SET. Our SGCL-SET can be trained to effectively learn the translation for the regions of confusing contexts using the segmentation and label information. Through comprehensive experiments on our KOMPSAT dataset, our SGCL-SET significantly outperforms all the previous methods with large margins across nine image quality evaluation metrics. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Segmentation-Guided Context Learning Using EO Object Labels for Stable SAR-to-EO Translation | - |
dc.type | Article | - |
dc.identifier.wosid | 001134444500003 | - |
dc.identifier.scopusid | 2-s2.0-85181545809 | - |
dc.type.rims | ART | - |
dc.citation.volume | 21 | - |
dc.citation.beginningpage | 1 | - |
dc.citation.endingpage | 5 | - |
dc.citation.publicationname | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | - |
dc.identifier.doi | 10.1109/LGRS.2023.3344804 | - |
dc.contributor.localauthor | Kim, Munchurl | - |
dc.contributor.nonIdAuthor | Kim, Hyun-Ho | - |
dc.contributor.nonIdAuthor | Seo, Doochun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Image segmentation | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Radar polarimetry | - |
dc.subject.keywordAuthor | Synthetic aperture radar | - |
dc.subject.keywordAuthor | Generators | - |
dc.subject.keywordAuthor | Electo-optic effects | - |
dc.subject.keywordAuthor | Speckle | - |
dc.subject.keywordAuthor | Generative adversarial network | - |
dc.subject.keywordAuthor | SAR-to-EO translation | - |
dc.subject.keywordAuthor | synthetic aperture radar (SAR) image | - |
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