基于DBSCAN技术的SAR图像感兴趣区域鉴别  

DBSCAN-based ROI discrimination from SAR images

作　　者：王鹏达[1] 贺新毅[2] 

机构地区：[1]中国电子科学研究院,北京100041 [2]东南大学,南京210096

出　　处：《信息技术》2012年第6期104-107,110,共5页Information Technology

摘　　要：使用一种新奇的聚类方法从粗略检测后的SAR图像中提取感兴趣区域(ROI),再通过多特征提取和综合鉴别,去除虚警保留目标,为进一步的目标识别做准备。自动目标聚类是基于SAR图像的自动目标识别系统的难点之一,带有噪声的基于密度的聚类方法 (DBSCAN)可以发现任意形状的聚类目标,只依赖于两个不敏感的系统参数,通过区域判断缩减计算时间减少计算内存,很好地适应了自动目标识别的系统需要。多特征目标鉴别方案基于聚类结果,研究聚类得到的感兴趣区域,通过提取多种特征综合判断,有效去除了虚警。所述方法已应用于某SAR-ATR系统,得到了很好的应用体验。The purpose of the paper is to extract the region of interest （ROI） from the coarse detected synthetic aperture radar （SAR） images and discriminate if the ROI contains a target or not, so as to eliminate the false alarm, and prepare for the target recognition. The automatic target clustering is one of the most dittieult tasks in the SAR-image ATR system. The density-based spatial clustering of applications with noise （DBSCAN） relies on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN was first used in the SAR image processing, which has many excellent features ： only two insensitivity parameters （ radius of neighborhood and minimum number of points） are needed; clusters of arbitrary shapes which fit in with the coarse detected SAR images can be discovered; and the time and computer memory can be reduced. In the multi-feature ROI discrimination scheme, it extracts several target features which contain the geometry features like the area discriminator and Radon transform based target profile discriminator. The synthesized judgment effectively eliminates the false alarms. The above methods have been used in a SAR-ATR system and got good application experiences.

关 键 词：自动目标识别 SAR图像 聚类算法 DBSCAN 感兴趣区域鉴别 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]