The cloud identification and classification (CIC) algorithm for high spectral resolution observations in the far- and mid-infrared part of the spectrum

Federico Donat; Elisa Fabbri; Tiziano Maestri; Michele Martinazzo; Fabrizio Masin; Giorgia Proietti Pelliccia; Lorenzo Cassini; Guido Masiello; Giuliano Liuzzi; Carmine Serio

doi:10.1117/12.3031709

20 November 2024 The cloud identification and classification (CIC) algorithm for high spectral resolution observations in the far- and mid-infrared part of the spectrum

Federico Donat, Elisa Fabbri, Tiziano Maestri, Michele Martinazzo, Fabrizio Masin, Giorgia Proietti Pelliccia, Lorenzo Cassini, Guido Masiello, Giuliano Liuzzi, Carmine Serio

Author Affiliations +

Proceedings Volume 13193, Remote Sensing of Clouds and the Atmosphere XXIX; 1319302 (2024) https://doi.org/10.1117/12.3031709
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

A Cloud Identification and Classification algorithm named CIC is illustrated. CIC is a machine learning method used for the classification of far and mid infrared radiances which allows to classify spectral observations by relying on small size training sets. The code is flexible meaning that can be easily set up and can be applied to diverse infrared spectral sensors on multiple platforms. Since its definition in 2019, the CIC has been applied to many observational geometries (airborne, satellite and ground-based) and is currently adopted as the scene classificator of the end-2-end simulator of the next ESA 9^th Earth Explorer, the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) which will spectrally observe the far infrared part of the spectrum with unprecedent accuracy. The algorithm has been recently improved to enhance its sensitivity to thin clouds (and also to surface features) and to increase the cloud hit rates in challenging conditions such as those characterizing the polar regions. The newly introduced metric is presented in details and the set-up procedures are discussed since they are critical for a correct application of the code. We illustrate the definition of the metric, the calibration process and the code optimization. The issues related to the definition of the reference training sets and to the classification of multiple classes are also presented.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Federico Donat, Elisa Fabbri, Tiziano Maestri, Michele Martinazzo, Fabrizio Masin, Giorgia Proietti Pelliccia, Lorenzo Cassini, Guido Masiello, Giuliano Liuzzi, and Carmine Serio "The cloud identification and classification (CIC) algorithm for high spectral resolution observations in the far- and mid-infrared part of the spectrum", Proc. SPIE 13193, Remote Sensing of Clouds and the Atmosphere XXIX, 1319302 (20 November 2024); https://doi.org/10.1117/12.3031709

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