Journal of Systems Engineering and Electronics

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles     Next Articles

Target detection and recognition in SAR imagery based on KFDA

Fei Gao1, Jingyuan Mei1, Jinping Sun1,*, Jun Wang1, Erfu Yang2, and Amir Hussain3   

  1. 1. School of Electronic and Information Engineering, Beihang University, Beijing 100191, China;
    2. Space Mechatronic Systems Technology Laboratory, Department of Design, Manufacture and Engineering Management, University of Strathclyde, Glasgow G1 1XJ, UK;
    3. Cognitive Signal-Image and Control Processing Research Laboratory, School of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK
  • Online:2015-08-25 Published:2010-01-03


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), especially for the detection and recognition of vehicles, an algorithm based on kernel fisher discriminant analysis (KFDA) is proposed. First, in order to make a better description of the difference between 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 recognition rate.