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Deep learning for extracting water body from Sentinel-2 MSI imagery

2021
<p>Deep learning has a good capacity of hierarchical feature learning from unlabeled remote sensing images. In this study, the simple linear iterative clustering (SLIC) method was improved to segment the image into good quality super-pixels.
openaire   +1 more source

Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study

Water Science and Technology: Water Supply, 2021
Jianli Ding   +2 more
exaly  

Floods automatic rapid mapping through Sentinel-2 MSI multitemporal data

Floods are widespread natural disasters on Earth affecting the planet with increasing frequency and intensity. Climate changes are responsible of the increasing number of heavy and persistent rains generating these destructive events often resulting in fatalities, injuries, and extensive infrastructural damages.
Valeria Satriano   +3 more
openaire   +1 more source

Atmospheric and sunglint correction for retrieving chlorophyll-a in a productive tropical estuarine-lagoon system using Sentinel-2 MSI imagery

ISPRS Journal of Photogrammetry and Remote Sensing, 2021
Matheus Henrique Tavares   +2 more
exaly  

Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites

European Journal of Remote Sensing, 2018
Julia A Barsi   +2 more
exaly  

Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

Remote Sensing, 2020
Pannimpullath Remanan Renosh   +2 more
exaly  

Space eye on flying aircraft: From Sentinel-2 MSI parallax to hybrid computing

Remote Sensing of Environment, 2020
Yongxue Liu   +2 more
exaly  

Assessment of Atmospheric Correction Algorithms for Sentinel-2 Msi Over Inland Lakes

2023
Qingyu Wang   +9 more
openaire   +1 more source

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