A neural network sea-ice cloud classification algorithm for copernicus sentinel-3 sea and land surface temperature radiometer

A Neural Network approach to classify Sentinel-3 sea and land surface temperature radiometer (SLSTR) pixels over polar regions is presented. The proposed approach is based on a careful preliminary analysis aimed to simulate SLSTR observation by means of MODIS data. The latter have been considered be...

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Bibliographic Details
Published in:IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Main Authors: Picchiani M., Del Frate F., Sist M.
Other Authors: Picchiani, M, Del Frate, F, Sist, M
Format: Conference Object
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2018
Subjects:
Cloud detection
Neural Network
Sentinel-3
Settore ING-INF/02 - CAMPI ELETTROMAGNETICI
Sea ice
Online Access:http://hdl.handle.net/2108/216365
https://doi.org/10.1109/IGARSS.2018.8517857
https://ieeexplore.ieee.org/document/8517857
Description
Summary:A Neural Network approach to classify Sentinel-3 sea and land surface temperature radiometer (SLSTR) pixels over polar regions is presented. The proposed approach is based on a careful preliminary analysis aimed to simulate SLSTR observation by means of MODIS data. The latter have been considered because of the long available time series and the quality of cloud mask products. A large set of MODIS AQUA and TERRA products has been applied to develop the training set of the Neural Network classificator that has been tuned to discriminate clear ocean, clouds and sea-ice surfaces on the scene.

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