Research On Target Clustering Algorithm Of SAR Image

Recently,the research of synthetic aperture radar is the hot topic both at home and abroad.In the modern war,the one who can quickly and accurately capture the enemy targets will take over the battlefield situation.This paper aim at explaining SAR image,for the purpose of developing practical target recognition system,do in-depth research in the three main links before object discrimination,there are SAR image noise reduction,target detection and cluster.In chapter one,first introduced the background and significance of the SAR image automatic target recognition(SAR-ATR);Then describe the research status of SAR image automatic target recognition technology and Shortcomings of them.At end of this chapter recommended the main work and content arrangement of this paper.The second chapter mainly did the research on algorithm of SAR image noise reduction.Provides detailed information on the principle of coherent noise.Introduced and compared several common algorithms of noise reduction.Using the experimental data which obtained from real SAR images to analyze the usability of the algorithms;explored the influence of filtering which caused by the size of the filter window.The third chapter mainly researched the SAR image target detection algorithm.Firstly,combined previous research results with the actual demand,considered the real-time demand,this paper has used Constant False Alarm Rate,CFAR,which widely applied to the practical problems as its characteristics of fast speed.through the experiment analyzed the effects of algorithm that caused by the size of the threshold and verified the effectiveness of the double parameter CFAR algorithm.In chapter four mainly studied SAR image target cluster technology.DBSCAN algorithm can effectively deal with clusters of arbitrary shape,but this algorithm is not an effective analysis of SAR images for its indistinctive properties.To sove this problem,this chapter proposes a new SAR images clustering algorithm based on DBSCAN.Firstly,the information of aircraft piecemeal parts is calculated by small area clustering.Then the aircraft number and fuselage position are obtained based on the size estimation by template and large area clustering.Lastly,aircraft category information emerges by using division method.The experimental result demonstrates that our new algorithm can get higher clustering accuracy compared to original algorithm.The fifth chapter summarized the full text of the work,and presented the direction of further research.