SAR Image Target Recognition Based On Support Vector Machine

Synthetic aperture radar (SAR) has been widely applied to national economic fields and military reconnaissance fields because of its all-weather, wide, strong penetrable ability and supplying detailed ground mapping material and images in atrocious weather with high resolution. The collection capacity for SAR images is growing rapidly, and along with that growth is the expanding need for exploitation of SAR images accurately and efficiently. Based on machine learning theory, developing the practical and effective classifier is of great importance. Support vector machines (SVM), which are based on statistical learning theory, are considered good candidates because of their high generalization performance even when the dimension of the input space is very high and the problem is nonlinear.This thesis studies SAR image feature extraction and target recognition based on SVM. The main contents and contributions are as follows: Firstly, reviews the development of SAR ATR techniques. Secondly, the invariant moments feature extraction method is proposed. Its efficiency is proved moment invariant with scale, translation and rotating invariance. Thirdly, a SAR image target recognition algorithm using SVM is introduced. The results demonstrate the effectiveness of this method. Finally, The SVM training time is reduced dramatically owing to a fast SVM training method which gets support vectors in advance and then uses an iterative and circulative strategy for training.In simulation experiments, we recognize the images using the invariant moments and SVM, Its successful rate is very high, therefore the results show that this method is valuable.