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.
Picchiani, M., Del Frate, F., Sist, M. (2018). A neural network sea-ice cloud classification algorithm for copernicus sentinel-3 sea and land surface temperature radiometer. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp.3015-3018). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS.2018.8517857].
A neural network sea-ice cloud classification algorithm for copernicus sentinel-3 sea and land surface temperature radiometer
Picchiani M.;Del Frate F.;Sist M.
2018-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
