Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6723342
