Research On Several Key Technologies Of SAR Image Processing And Target Identification

Target identification of Synthetic Aperture Radar (SAR) image is a judgment for attribute,category, and type of target. The SRA image, based on target identification, plays an important role onthe field of military and civilian use, etc. With the sharply expansion of SAR data origin, how toovercome the influences of speckle noise, extract useful information in a fast and accurate way, andrealize the target identification of SAR image, all of these are the challenges for SAR imageapplication. This paper aims at some key technologies in SAR ATR, such as speckle reduction, targetsegmentation, feature extraction and target identification, etc. to develop research. The main finishedwork as follows:(1) Study speckle reduction that based on Ridgelet redundant dictionary. The problems ofindistinct borders and textures of SAR that caused by wavelet dictionary builds dictionary based onstrangeness, propose a kind of speckle reduction algorithm of SAR image in line with Ridgeletredundant dictionary. Establish redundant dictionary by using Ridgelet conversion with strongerdescription of alignment strangeness. Realize the sparse representation of SAR image. Complete thespeckle reduction of SAR image. According to the experimental results, comparing this algorithmwith the algorithm of speckle reduction with traditional wavelet, the PSNR has improved about5dB.(2)Study the target segmentation of SAR image that based Maxflow of energy. Under thelarge-scale scene of SAR, the traditional partitioning algorithm extracts target with overtoppedfalse-alarm, resulting in needing major artificial interactive operation, so the problem of automaticsegmentation to target can not be realized. Translate the target segmentation into a problem of solvingMaxflow of energy. Put forward a kind of segmentation method of SAR image that based on Maxflowof energy. Calculate the Maxflow of SAR image in virtue of Ford-Fukerson algorithm. Extract targetand dash area. Combine with a kind of modified Otus algorithm to clearly divide target and shadow.According to the experimental results, comparing with two-parameter CFAR, this algorithm reducethe false-alarm of target segmentation of SAR image, it is also increased in the cost function and timeefficiency.(3) Study identification method with sparse representation of SAR image that based on manifoldmodeling. The problem of non-ideal classification results that caused by the traditional featurepick-up algorithm of overall situation linear structure hypothesis can’t effectively overcome theproblem of high-dimensional SAR dataset’s nonlinearity influences. Combine the manifold learning with sparse representation. According to the characteristic of the same kind of SAR image lies in thesame low-dimensional manifold, propose a kind of identification method with sparse representation ofSAR image that based on manifold modeling. Use mixture factor analysis model to conduct manifoldmodeling for MSTAR. Obtain characteristic parameter of various targets by using EM algorithm andstructure a global dictionary. Finally, determine the coefficient of sparse representation by utilizingiteration of OMP algorithm and classify the target. According to the experimental results, the targetrecognition rate towards the three kinds of MSTAR is over97%. Favorable classification results havebeen obtained, which proves the feasibility of algorithm.