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.