Dr. Maria Francesca Carfora Profile

Dr. Maria Francesca Carfora

at CNR

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Proceedings Article | 9 October 2019 Presentation + Paper

Assessment of cumulative discriminant analysis for cloud detection in the ESA PROBA-V Round Robin exercise

Umberto Amato, Maria Francesca Carfora, Guido Masiello, Carmine Serio

Proceedings Volume 11152, 1115207 (2019) https://doi.org/10.1117/12.2534292

KEYWORDS: Clouds, MODIS, Statistical analysis, Error analysis, Detection and tracking algorithms, Sensors, Reflectivity, Spatial resolution, Satellites

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Cloud detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking can be translated directly into significant uncertainty in the retrieved downstream geophysical products. The problem is particularly challenging when only of a limited number of spectral bands is available, and thermal infrared bands are lacking. This is the case of Proba-V instrument, for which the European Space Agency (ESA) carried out a dedicated Round Robin exercise, aimed at intercomparing several cloud detection algorithms to better understand their advantages and drawbacks for various clouds and surface conditions, and to learn lessons on cloud detection in the VNIR and SWIR domain for land and coastal water remote sensing. The present contribution is aimed at a thorough quality assessment of the results of the cloud detection approach we proposed, based on Cumulative Discriminant Analysis. Such a statistical method relies on the empirical cumulative distribution function of the measured reflectance in clear and cloudy conditions to produce a decision rule. It can be adapted to the user's requirements in terms of preferred levels for both type I and type II errors. In order to obtain a fully automatic procedure, we choose as a training dataset a subset of the full Proba-V scenes for which a cloud mask is estimated by a consolidated algorithm (silver standard), that is from either SEVIRI, MODIS or both sensors. Within this training set, different subsets have been setup according to the different types of surface underlying scenes (water, vegetation, bare land, urban, and snow/ice). We present the analysis of the cloud classification errors for a range of such test scenes to yield important inferences on the efficiency and accuracy of the proposed methodology when applied to different types of surfaces.

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