A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images

A novel runway detection algorithm for PolSAR (Polarimetric Synthetic Aperture Radar) images based on optimized polarimetric features and local spatial information is proposed. Existing methods for runway detection for PolSAR images always utilize the parallel line as the primary feature. However, many other ground objects such as rivers and roads also have parallel structures thus affect the performance of these detection methods. The proposed method is based on two stages of classification with polarimetric features and the HOG (Histogram of Oriented Gradient) feature, while avoiding the interference due to the similar morphological features among different ground objects. An FCBF (Fast Correlation Based Filter) is firstly used for optimizing and selecting of the ground objects’ polarimetric features of ground targets. Then RF (Random Forest) classifier is employed for extracting ROIs (Region of Interest) which may contain runways. Then HOG features are extracted from these ROIs for further classification with SVM (Support Vector Machines) to detect the runway area. Experimental results with the measured PolSAR data provided by NASA UAVSAR project show that the proposed method can detect runway regions effectively without using the parallel line. Comparative analysis is also conducted on parallel line pattern based algorithms. And the results suggest the effectiveness and performance enhancement of this method.