Statistical cloud detection from SEVIRI multispectral images

被引:56
作者
Amato, U. [1 ]
Antomadis, A. [2 ]
Cuomo, V. [3 ]
Cutillo, L. [1 ]
Franzese, M. [1 ,4 ]
Murino, L. [1 ,5 ]
Serio, C. [4 ]
机构
[1] CNR, Sede Napoli, Ist Applicaz Calcolo M Picone, I-00185 Rome, Italy
[2] Univ Grenoble 1, Lab Jean Kuntzmann, Grenoble, France
[3] CNR, Ist Metodol Anal Ambientale, Potenza, Italy
[4] Univ Basilicata, Dipartimento Ingn & Fis Ambientale, I-85100 Potenza, Italy
[5] Ctr Reg Competenza Anal & Monitoraggio Rischio Ca, Naples, Italy
关键词
clouds; multispectral; classification; geostationary; MSG; SEVIRI; discriminant analysis;
D O I
10.1016/j.rse.2007.06.004
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Cloud detection from geostationary satellite multispectral images through statistical methodologies is investigated. Discriminant analysis methods are considered to this purpose, endowed with a nonparametric density estimation and a linear transform into principal and independent components. The whole methodology is applied to the MSG-SEVIRI sensor through a set of test images covering the central and southern part of Europe. "Truth" data for the learning phase of discriminant analysis are taken from the cloud mask product MOD35 in correspondence of passages of MODIS close to the SEVIRI images. Performance of the discriminant analysis methods is estimated over sea/land, daytime/nighttime both when training and test datasets coincide and when they are different. Discriminant analysis shows very good performance in detecting clouds, especially over land. PCA and ICA are effective in improving detection. (C) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:750 / 766
页数:17
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