Multisensor Fusion and Deep Learning Approaches for Semantic Segmentation of Glacial Lakes: A Comparative Study for Coastal Hydrology Applications

Monitoring glacial lakes is critical for assessing climate change impacts and mitigating glacial lake outburst flood risks. This study evaluates three deep learning architectures, U-Net, simple convolutional neural network (CNN), and atrous spatial pyramid pooling SegNet (ASPP SegNet), for binary se...

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Autors principals:	Lingling Xue, Asad Khan, Muhammad Haseeb, Mourad Aqnouy, Dawood Ahmad, Refka Ghodhbani, Dmitry E. Kucher, Olga D. Kucher
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2025-01-01
Col·lecció:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Matèries:	Atrous spatial pyramid pooling SegNet (ASPP SegNet) coastal monitoring deep learning glacial lake remote sensing semantic segmentation
Accés en línia:	https://ieeexplore.ieee.org/document/11021621/
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