One airport detection method based on support vector machine

This paper proposes a novel airport detection method, which integrates the texture features and shape features of the airport. Eight texture features, such as the mean of the region, the deviation of the region, the smoothness of the region, the skewness of a histogram, the uniformity of the region, the randomness of the region, the mean of the gradient image and the deviation of the gradient image, are used to represent the features of the region. In this method, first the long lines are detected and the regions where the lines locate are segmented. Second, support vector machine (SVM) based on Gaussian kernel is used as a classifier which discriminates the runway from other candidate regions. Experimental results show that the error rate of the proposed method is lower than those of conventional methods which detect airport only by the shape feature of runway. The detection accuracy of the proposed method is nearly ten times higher than that of Liu's methods, and the method has favorable speed for a real-time system.