A Study Of Road And Bridge Targets Recognition In SAR Imagery

Road and Bridge are the main land traffic facilities in the world today, while they are also the important targets in the military field. Synthetic Aperture Radar (SAR) has been widely applied to gain large-area and high-resolution images, all-day and all-weather. These make SAR widely used in aerospace, ground reconnaissance, remote sensing or resource census etc. As the recognition of road and bridge is very useful for image registration, precision-guidance, map drawing and target detection and so on, the research on this field becomes more and more important.Based on the features of road and bridge, this paper mainly researched on the choosing of features and target recognition of road and bridge with the background of SAR image understanding.This paper firstly study on several traditional filters and some filters based on coherent speckle, and choose Lee filter to pre-process the original images, for Lee filter can de-noise well and reserve the edge information effectively. Also we talk about some image segmentation algorithms to get the theoretical basis of the recognition steps.Based on analyzing kinds of features, we choose shape and radiate features to recognize road targets, as these features are easy to extract and use. We advised a road recognition algorithm based on dynamic programming, which is proved to be effective on recognition and orientation, with high rate of recognition and low rate of false target. However, the result still has some problems about rupture and false target because of the noise and shadow.Based on analyzing kinds of features, we choose shape and radiate features to recognize bridge targets, as these features are easy to extract and use. First, we advised an algorithm based on the shape features, such as parallels, length-width ratio, position information and so on. Experiment results show the good qualities of the algorithm, such as fast speed, high rate of recognition, and low rate of false target. However, since the shape may be distorted in SAR images by the noise, we proposed a novel algorithm for bridge recognition using Histogram Entropy presented by Pun which is stable on shape distortion. This algorithm increased the recognition rate, decreased the rate of false target, and showed robustness in several conditions, while the time cost and algorithm complexity are not increased evidently.