Journal on Communications ›› 2020, Vol. 41 ›› Issue (3): 91-101.doi: 10.11959/j.issn.1000-436x.2020013

• Papers • Previous Articles     Next Articles

Ship detection optimization method in SAR imagery based on multi-feature weighting

Quanhua ZHAO1,Xiao WANG1,Yu LI1,Guanghui WANG2   

  1. 1 School of Geomatics,Liaoning Technical University,Fuxin 123000,China
    2 Satellite Surveying and Mapping Application Center,NASG,Beijing 100048,China
  • Revised:2019-12-14 Online:2020-03-25 Published:2020-03-31
  • Supported by:
    The Project of Science and Technology Research of Education Department of Liaoning Province(LJ2019JL001)

Abstract:

Aiming at the problem that the accuracy of traditional ship detection algorithms is not satisfying in complex scene with many false alarm targets,a ship detection optimization method in SAR imagery based on multi-feature weighting was proposed.Firstly,the marker-based watershed algorithm was employed to remove land from SAR amplitude image.Then,the CFAR algorithm based on log-normal distribution was used to obtain candidate targets from no land image.Furthermore,the length to width ratio,the ship area and the contrast ratio of the candidate targets were extracted.Finally,a variance coefficient method was proposed to distribute the weight of the three features,and the confidence levels were calculated by combining the normalized feature vectors of the candidate targets with the feature weight.By determining the best confidence level,false alarm targets among the candidate targets were removed to optimize ship detection results.In order to verify the proposed method,experiments were carried on with the GF-3 SAR images of different complex scenes.The experimental results show that the proposed method is feasible and effective.

Key words: synthetic aperture radar, ship detection, log-normal distribution, constant false alarm rate, weight allocation

CLC Number: 

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