Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches

Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches

Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas e...

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Bibliographic Details
Main Authors: Bibek Aryal, Stephen M. Escarzaga, Sergio A. Vargas Zesati, Miguel Velez-Reyes, Olac Fuentes, Craig Tweedie
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Remote Sensing
Subjects:
land water segmentation
remote sensing
deep learning
sparse labels
Online Access:https://www.mdpi.com/2072-4292/13/22/4572

Internet

https://www.mdpi.com/2072-4292/13/22/4572

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