Recognition Of Synthetic Aperture Radar Image Target Based On Support Vector Machine

Synthetic aperture radar (SAR) is a system of high-altitude visual which has characters as all-weather, long distance, strong penetrable ability and high resolution. SAR target recognition has become a hotspot, the most important section is how to extract feature accurately and adopt effective classification recognition method. As the SAR image which is different from common optical image has serious problem with noise. And traditional image feature extraction method for SAR images is limited. Support Vector Machine (SVM) based on the statistical learning theory is a new method of machine learning. SVM which can settle small example problem well has been applied to many fields for its excellent learning and generalizing ability.This thesis mainly studies feature extraction and classification recognition of SAR image based on SVM. Firstly, reviews the research status and development trend of SAR target recognition and SVM. Secondly, studies the feature of SAR image and summarizes the general process of SAR target recognition. Then, we carried on the image de-noising and segmentation using adaptive filter and Markov random field. And the Hu invariant moments stability experiment is carried out, We delete the components of poor stability. According to the feature of SAR target, we recombine a set of invariant moments by combining Hu invariant moments with affine invariant moments to carry out feature extraction. This method not only has the characteristics of scale, translation and rotating invariance, but also affine invariance. Finally, introduce the SVM to training and recognition, then extend the binary classification to the multi-classification by directed acyclic graph.In series of experiments, the excellent recognition rate is achieved by using the recombined invariant moments feature extraction, therefore this method is valuable. We test the similarity for being identified target before recognition for reducing the probability of misrecognition and improving the robustness of the system.