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

作者：Gao, Fei; You, Jialing; Wang, Jun^*; Sun, Jinping; Yang, Erfu; Zhou, Huiyu

来源：Neurocomputing, 2017, 267: 220-231.

DOI：10.1016/j.neucom.2017.06.004

• Synthetic aperture radar (SAR) target detection
• Shadow
• Saliency detection
• Local spatial autocorrelation
• Visual features
• One-Class SVM (OC-SVM)

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 first 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 significance 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 different imaging environments.

• 出版日期2017-12-6
• 单位北京航空航天大学