Enhancing Detection of Inundated Areas Using Novel Hybrid PolSAR-Metaheuristic-Deep Learning Models

Floods are renowned as the most destructive natural phenomena, and their frequency and intensity increase due to climate change. Accurate and timely flood mapping is critical for effective risk mitigation. However, traditional approaches relying on optical remote sensing imagery and synthetic apertu...

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Główni autorzy:	Seongjun Lee, Fatemeh Rezaie, Ebony Lee, Subin Kim, Hoang Hai Nguyen, Dowon Lee, Ali A. Alesheikh, Mahdi Panahi, Zahra Kalantari, Minha Choi, Fangni Lei, Hyunglok Kim
Format:	Artigo
Język:	Inglês
Wydane:	IEEE 2026-01-01
Seria:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hasła przedmiotowe:	Deep learning (DL) flood polarimetric synthetic aperture radar (PolSAR) semantic segmentation
Dostęp online:	https://ieeexplore.ieee.org/document/11568476/
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