کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947003 | 1439560 | 2017 | 40 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel target detection method for SAR images based on shadow proposal and saliency analysis
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A novel target detection method for SAR images based on shadow proposal and saliency analysis A novel target detection method for SAR images based on shadow proposal and saliency analysis](/preview/png/4947003.png)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 267, 6 December 2017, Pages 220-231
Journal: Neurocomputing - Volume 267, 6 December 2017, Pages 220-231
نویسندگان
Fei Gao, Jialing You, Jun Wang, Jinping Sun, Erfu Yang, Huiyu Zhou,