Sar Image Target Feature Extraction And Classification Study

Recently, SAR is evolving to become an indispensable reconnaissance tool for military purposes. The collection capacity for SAR images is growing rapidly, and along with that growth is the expanding need for automated or semi-automated exploitation of SAR images accurately and efficiently. SAR ATR is an important aspect of automatic or semi-automatic SAR image interpretation. Model-based SAR ATR system that uses feature is the trend for SAR ATR. Methods of feature extraction and classification of target in model-based SAR ATR using feature are studied systemically in this paper.Peak feature is very important for SAR ATR. In order to extract target's peak from SAR image rapidly and accurately, the method of target's peak feature extraction is firstly studied in this dissertation. And a method of target's peak automatic extraction in sub-pixel accuracy is proposed, it can estimate the position of peak in sub-pixel accuracy. For enhancing target's peak feature with SAR imaging parameters given, the peak-enhanced SAR imaging method is studied. Based on work of W. Clem Karl etc., the quasi-Newton iteration method for solving the optimization problem of SAR imaging is derived, the sparse projection matrix Tsof SAR imaging is constructed, by replacing the original SAR imaging projection matrix T with TS, the computation efficiency of peak-enhanced SAR imaging is improved greatly. In order to improve the efficiency of classification based on feature matching, the method of azimuth estimation from SAR image is studied. A method of target's azimuth estimation from SAR image using peak feature based on linear regression is proposed, besides goodish estimation accuracy and high computation efficiency, it can also provide the confidence interval of the estimation, which can meet the need of model-based SAR ATR system that uses feature very well.Attributed scattering center comprises more features for SAR ATR, but because of its high dimensionality, the method of attributed scattering center extraction is more complicated. In order to put attributed scattering center 'into SAR ATR use, the method of attributed scattering center extraction is studied in chapter 3. And the RD(Region Decoupled)-AML(Approximate Maximum Likelihood)-CLEAN method is presented for extracting attributed scattering center from SAR image, it can extract scattering center from SAR image quickly and automatically.Based on the method of feature extraction, the method of model-based SAR target classification using feature is studied. The model-based SAR target classifier that uses feature accomplishes classification by computing the likelihood function between extractedand predicted features. Computation the likelihood function requires using the correspondences between extracted and predicted features. Taking attributed scattering center-based classification as example, the computation of feature likelihood function under many-many and 1-1 correspondence are studied, by using the algorithm of bipartite graph perfect matching to find the optimal 1-1 correspondence, the computation efficiency is improved greatly, the relations of likelihood function between 1-1 and many-many correspondence are analyzed, and two sub-optimal methods of calculating the likelihood function of 1-1 correspondence are presented.Finally, based on preceding chapters, an experiment system of model-based SAR target classification using feature is devised in chapter 5, based on this system, through large number of experiments using MSTAR SAR image, the validities of methods of feature extraction and classification are verified, effects of several factors on performance of SAR target classification are analyzed thoroughly and systemically.