SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels

Deep learning-based SAR oil spill detection faces significant challenges due to limited labeled training data. To address this, we propose SinGAN-Labeler, an enhanced framework that generates high-quality synthetic SAR oil spill images and their labels from minimal input. The model integrates an ada...

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Autori principali:	Bin Wang, Lei Chen, Dongmei Song, Weimin Chen, Jintao Yu
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2025-02-01
Serie:	Journal of Marine Science and Engineering
Soggetti:	marine oil spill generative adversarial network (GAN) hybrid attention mechanism SAR image expansion
Accesso online:	https://www.mdpi.com/2077-1312/13/3/422
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