Target recognition of SAR images by partially matching of target outlines

Abstract

A synthetic aperture radar (SAR) automatic target recognition method is proposed based on the matching of the target outlines. The target outline describes the physical sizes and shape of the target thus discriminative for SAR target recognition. The original target outline is segmented into several independent parts. The distance between each part and its counterpart in the corresponding template is measured by the least-trimmed square Hausdorff distance. Afterwards, the results of individual parts are combined to form a similarity measure, which comprehensively considers the possible deformations of the target outline. Based on the similarity measure, the target type is determined to be the class sharing the maximum similarity with the test sample. To evaluate the performance of the proposed method, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under both the standard operating condition and several typical extended operating conditions.

Keywords:

• Synthetic aperture radar
• automatic target recognition
• target outline
• least-trimmed square Hausdorff distance

Additional information

Funding

This work was partly supported by Beijing Municipal Natural Science Foundation (Grant no. Z8162039), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19080100), the Hainan Provincial Department of Science and Technology (Grant no. ZDKJ2016021), the Natural Science Foundation of Hainan Province (Grant no. 20154171) and the 135 Plan Project of Chinese Academy of Sciences (Grant no. Y6SG0200CX).

Notes on contributors

Jian Tan

Jian Tan has got his Doctor Degree in WEB Geographic Information Science in 2008, and mainly engaged in the research of the spatial information system, graphic image processing and computational graphics. Now he is working at the Key Laboratory of Earth Observation of Hainan Province and Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences, CAS (Visualization Technology Department, International Centre on Space Technologies for Natural and Cultural Heritage under the auspices of UNESCO).

Xiangtao Fan

Xiangtao Fan is the director of the Department of Digital Earth application and theory at Key Laboratory of earth observation of Hainan Province and Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences, CAS (Visualization Technology Department, International Centre on Space Technologies for Natural and Cultural Heritage under the auspices of UNESCO), and mainly engaged in the research of remote sensing applications, graphic image processing and the digital earth system.

Shenghua Wang

Shenghua Wang is an associate professor in the School of Public Administration and Mass Media, Beijing Information Science and Technology University, Beijing, People's Republic of China, and she is mainly engaged in the research of graphic image processing and mass media.

Yingchao Ren

Yingchao Ren is an associate researcher in the Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, People's Republic of China, and he is mainly engaged in the research of remote sensing applications and the geographic system.

Changshun Guo

Changshun Guo received a BS degree from Shandong Jianzhu University, Jinan, China, in 2013. He is currently a master's student in the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. His research interests include digital earth and visualization.

Jian Liu

Jian Liu is an associate researcher in the Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, People's Republic of China, and she is mainly engaged in the research of remote image processing and the geographic system.

Jing Li

Jing Li is an associate researcher in the Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, People's Republic of China, and he is mainly engaged in the research of remote image applications and the geographic location system.

Qin Zhan

Qin Zhan is an associate researcher in the Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, People's Republic of China, and she is mainly engaged in the research of meta data processing and the geographic system.