A fast cloud detection approach by integration of image segmentation and support vector machine

Authors:
Bo Han;Lishan Kang;Huazhu Song
Affiliations:
School of Computer Science, Wuhan University, Wuhan, Hubei, P.R. China;School of Computer Science, Wuhan University, Wuhan, Hubei, P.R. China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

We proposed a fast cloud detection approach for the geophysical data from Moderate Resolution Imaging Spectroradiometer (MODIS), a premium instrument aboard on NASA’s satellite Terra to study clouds and aerosols. Previous pixel-based classifiers have been developed for remote-sensing instruments using various machine learning techniques, such as artificial neural networks (ANNs), support vector machines (SVMs). However, their computational costs are very expensive. Our novel approach integrated image segmentation and SVMs together to achieve the similar classification accuracy while using much less computation costs. It exploited the homogeneous property in local spatial sub-regions and used radiance information from sub-regions, rather than pixels, to build classifiers. The experimental results showed the proposed approach not only greatly speed up the classification training procedure, but also provide insights for domain experts to reveal different cloud types.