A novel target detection method for SAR images based on shadow proposal and saliency analysis

Fei Gao, Jialing You, Jun Wang, Jinping Sun, Erfu Yang, Huiyu Zhou

Research output: Contribution to journalArticle

14 Citations (Scopus)
12 Downloads (Pure)

Abstract

Conventional synthetic aperture radar (SAR) based target detection methods generally use high intensity pixels in the pre-screening stage while ignoring shadow information. Furthermore, they cannot accurately extract the target area and also have poor performance in cluttered environments. To solve this problem, a novel SAR target detection method which combines shadow proposal and saliency analysis is presented in this paper. The detection process is divided into shadow proposal, saliency detection and One-Class Support Vector Machine (OC-SVM) screening stages. In the shadow proposal stage, localizing targets is performed rst with the detected shadow regions to generate proposal chips that may contain potential targets. Then saliency detection is conducted to extract salient regions of the proposal chips using local spatial autocorrelation and signicance tests. Afterwards, in the last stage, the OC-SVM is employed to identify the real targets from the salient regions. Experimental results show that the proposed saliency detection method possesses higher detection accuracy than several state of the art methods on SAR images. Furthermore, the proposed SAR target detection method is demonstrated to be robust under dierent imaging environments. to extract salient regions of the proposal chips using local spatial autocorrelation and signicance tests. Afterwards, in the last stage, the OC-SVM is employed
Original languageEnglish
Pages (from-to)220-231
Number of pages12
JournalNeurocomputing
Volume267
Early online date8 Jun 2017
DOIs
Publication statusPublished - 6 Dec 2017

Keywords

  • synthetic aperture radar (SAR) target detection
  • shadow
  • saliency detection
  • local spatial autocorrelation,
  • visual features
  • one-class SVM (OC-SVM)

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