Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea

Satellite-derived bathymetry (SDB) has emerged as a critical technique in response to the growing demand for large-scale coastal bathymetric mapping. However, high-resolution multispectral imagery from Gaofen satellites presents significant challenges owing to low signal-to-noise ratios (SNRs). This...

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Автори:	Congshuang Xie, Siqi Zhang, Zhenhua Zhang, Peng Chen, Delu Pan
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Elsevier 2025-07-01
Серія:	Ecological Informatics
Предмети:	Coastal bathymetry Satellite-derived bathymetry (SDB) Physics-informed recurrent neural network (PI-RNN) Gaofen satellite imagery ICESat-2/ATLAS Lidar Adaptive ellipse DBSCAN (AE-DBSCAN)
Онлайн доступ:	http://www.sciencedirect.com/science/article/pii/S157495412500130X
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