Feature extraction in developing an airs cloud mask

Author

Marais, Willem ; Yu Hen Hu ; Holz, Ralph

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Wisconsin-Madison, Madison, WI, USA

fYear

2013

fDate

21-26 July 2013

Firstpage

2551

Lastpage

2554

Abstract

Cloud and clear-sky detection is a crucial part in the analyses of AIRS (Atmospheric InfraRed Sensor) measurements. Currently cloud detection is done using spectral tests, which are based on well understood properties of the atmosphere. This paper gives an account of an investigation in using binary classification and feature extraction techniques to develop an AIRS cloud mask, where CALIOP (Cloud-Aerosol LIDAR with Orthogonal Polarization) observations were used as “oracle” data. The objective was to produce an AIRS cloud mask which is either on par or better than the MODIS (Moderate Resolution Imaging Spectro-radiometer) cloud mask.

Keywords

atmospheric techniques; clouds; feature extraction; geophysical image processing; AIRS cloud mask; AIRS measurements; Atmospheric InfraRed Sensor; CALIOP observations; Cloud-Aerosol LIDAR with Orthogonal Polarization; MODIS cloud mask; Moderate Resolution Imaging Spectro-radiometer; atmosphere properties; binary classification; clear-sky detection; feature extraction technique; oracle data; Clouds; Error analysis; Feature extraction; MODIS; Support vector machines; Training; Vectors; Clouds; Feature extraction; Pattern recognition; Remote sensing; Support Vector Machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International

Conference_Location

Melbourne, VIC

ISSN

2153-6996

Print_ISBN

978-1-4799-1114-1

Type

conf

DOI

10.1109/IGARSS.2013.6723342

Filename

6723342