4 August 2009 Application of support vector machines in cloud detection using EOS/MODIS
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Focused on the cloud detection task using EOS/MODIS (Earth Observation System/Moderate Resolution Imaging Spectroradiometer) information, this paper introduced a new method for cloud detection by use of the Support Vector Machines (SVMs) algorithm. Firstly, the paper introduces the bands used in cloud detection and analyzes the process of feature selection. Secondly, special software named Libsvm was introduced, which was widely used in support vector classification and support vector regression. Then the new method is established by building a model of remote sensing image classification based on SVMs. The performance of SVMs was compared with the prevailing method of error back propagation neural network (BP-NN) method with different training set numbers from 2000 to 250. 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 smaller (with a number of 500 or less). There is no significant difference of error rate among three kernel functions of SVMs algorithm. The general error rate is about 4%. Thirdly, in order to valuate the capability of SVMs, two cases were selected for cloud detection using SVMs method. The cloud was clearly identified either from land or sea, and the snow cover existed in both cases, which was selected intentionally to test the capability of distinguishing the cloud from the underneath snow. 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.
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Yingming He, Hanjie Wang, and Hao Guan "Application of support vector machines in cloud detection using EOS/MODIS", Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73830O (4 August 2009); doi: 10.1117/12.834833; https://doi.org/10.1117/12.834833

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