Detection of ship targets based on CFAR-DCRF in single infrared remote sensing images
Received:January 10, 2019  Revised:May 28, 2019  download
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Author NameAffiliationE-mail
SONG Wen-Tao Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai, 200083, China
Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083 China
University of Chinese Academy of Sciences, Beijing 10000, China 
songwentao@whu.edu.cn 
HU Yong Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai, 200083, China
Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083 China 
huyong@mail.sitp.ac.cn 
KUANG Ding-Bo Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai, 200083, China  
GONG Cai-Lan Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai, 200083, China
Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083 China 
 
ZHANG Wen-Qi Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai, 200083, China
Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083 China
University of Chinese Academy of Sciences, Beijing 10000, China 
 
HUANG Shuo Shanghai Institute of Technical Physics of Chinese Academy of Sciences, Shanghai, 200083, China
Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083 China
University of Chinese Academy of Sciences, Beijing 10000, China 
 
Abstract:This paper focuses on the problem of low detection accuracy and low pixel image extraction accuracy of traditional small target detection and ship detection methods. An improved target detection algorithm based on constant false-alarm rate( CFAR )- dense conditional fandom fields ( DCRF)is proposed. The algorithm is based on the characteristics of small target and false alarm signal changes but different structural features. It uses the advantages of conditional fandom fields (CRF) multi-dimensional context (space, radiation) to achieve false alarm feature suppression, and introduces CFAR to improve the model and improve DCRF. Based on this model, experiments were performed under different conditions. The analysis results show that the algorithm can make full use of the global context information of the sea area, and can reduce the false alarm rate while maintaining a high detection rate.
keywords:remote sensing  dense conditional random fields  infrared dim target  constant false-alarm rate (CFAR)
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