Segmentation-Guided Context Learning Using EO Object Labels for Stable SAR-to-EO Translation

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2024-01
Language
English
Article Type
Article
Citation

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v.21, pp.1 - 5

ISSN
1545-598X
DOI
10.1109/LGRS.2023.3344804
URI
http://hdl.handle.net/10203/317877
Appears in Collection
EE-Journal Papers(저널논문)
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