Target detection and recognition in SAR imagery based on KFDA  

作　　者：Fei Gao Jingyuan Mei Jinping Sun Jun Wang Erfu Yang Amir Hussain 

机构地区：[1]School of Electronic and Information Engineering, Beihang University [2]Space Mechatronic Systems Technology Laboratory, Department of Design, Manufacture and Engineering Management, University of Strathclyde [3]Cognitive Signal-Image and Control Processing Research Laboratory, School of Natural Sciences,University of Stirling

出　　处：《Journal of Systems Engineering and Electronics》2015年第4期720-731,共12页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61071139;61471019;61171122);the Aeronautical Science Foundation of China(20142051022);the Foundation of ATR Key Lab(C80264);the National Natural Science Foundation of China(NNSFC)under the RSE-NNSFC Joint Project(2012-2014)(61211130210)with Beihang University;the RSE-NNSFC Joint Project(2012-2014)(61211130309)with Anhui University;the"Sino-UK Higher Education Research Partnership for Ph D Studies"Joint Project(2013-2015)

摘　　要：Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.

关 键 词：synthetic aperture radar （SAR） target detection ker-nel fisher discriminant analysis （KFDA） target recognition imageEuclidean distance （IMED） support vector machine （SVM）. 

分 类 号：TN957.52[电子电信—信号与信息处理]