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20 September 2020 Cloud detection from IASI hyperspectral data: a statistical approach based on neural networks
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Abstract
In this work, an investigation of the capability of a statistical cloud detection scheme, implemented through the use of a multilayer feed-forward neural network, is assessed. The whole methodology is applied to a set of IASI L1C spectral radiances, covering the period January 2016-November 2016 and related to Eastern Europe and tropical areas. From a subsampled training dataset where the sky conditions are “certainly” known, we have performed the supervised learning of statistical features of the cloudy- and clear- sky conditions, where truth data have been taken from a cloud mask product of the Advanced Very High-Resolution Radiometer (AVHRR). Also, to improve the neural network classification performances: i) Principal Component Analysis (PCA) of IASI spectra and ii) neural network learning regularization techniques, have been used. Finally, the neural network classification analysis, evaluated during the training with a validation dataset and then with a test dataset, shows very good performance in detecting clouds, with an accuracy of about 93%.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pietro Mastro, Pamela Pasquariello, Guido Masiello, and Carmine Serio "Cloud detection from IASI hyperspectral data: a statistical approach based on neural networks", Proc. SPIE 11531, Remote Sensing of Clouds and the Atmosphere XXV, 115310D (20 September 2020); https://doi.org/10.1117/12.2573326
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