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Proceedings Paper

Application of support vector machines in cloud detection using EOS/MODIS
Author(s): Hanjie Wang; Yinming He; Hao Guan
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Paper Abstract

Focused on the cloud detection task using EOS/MODIS information, this paper introduced a new method of cloud detection by use of the Support Vector Machines (SVMs) algorithm. The performance of SVMs was compared with the prevailing method of BP neural network (BP-NN) method with different training set numbers. The two methods show similar detection accuracy when the training set number is larger (with a number larger than 1500), while SVMs perform better than BP-NN method when the sampling number is small (with a number of 250 or less). SVMs method was then used to detect cloud over both land and sea; it distinguished cloud from snow cover, water body, and other land surface objectives clearly. Therefore, the SVMs technique is proved effective as compared with traditional methods in remote sensing image classification and is worthwhile to be popularized in the society of remote sensing applications.

Paper Details

Date Published: 25 August 2008
PDF: 8 pages
Proc. SPIE 7088, Remote Sensing Applications for Aviation Weather Hazard Detection and Decision Support, 70880M (25 August 2008); doi: 10.1117/12.792688
Show Author Affiliations
Hanjie Wang, Key Lab. of Regional Climate-Environment for Temperate East Asia (China)
Yinming He, PLA Univ. of Science and Technology (China)
Hao Guan, PLA Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 7088:
Remote Sensing Applications for Aviation Weather Hazard Detection and Decision Support
Wayne F. Feltz; John J. Murray, Editor(s)

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