A cloud image detection method based on SVM vector machine

The satellite remote sensing image data volume is too big, therefore, transmitting, storing and processing mass data is very difficult. Thus, the current methods may not perform well. In this paper, we propose a cloud image detection method based on SVM vector machine to remove thick cloud data to reduce the amount of data to improve the efficiency of the data. Firstly, the satellite remote sensing image is divide into small blocks, and the brightness characteristics of the sub-block image is extracted to accomplish the preliminary detection. Then the average gradient and the angle of the gray level co-occurrence matrix second-order moment for sub-block image based on the texture features of the sub-block image is calculated as the basic of SVM victor machine. The sub-block cloud image is used as learning samples of the SVM classifier that has brightness characteristics, and the classification model is obtained from the training of the SVM classifier to realize a detail classification of the cloud image detect based on the SVM victor machine. Finally, we conduct experiments on cloud image detection method based on SVM vector machine. Experiment results demonstrate detection accuracy of the method proposed could reach above 90%. (C) 20115 Published by Elsevier B.V.