基于Wishart距离和超像素的极化SAR图像车辆检测

doi:10.12305/j.issn.1001-506X.2021.10.08

Abstract

Abstract:

Aiming at the problem of dense vehicle target detection in polarimetric synthetic aperture radar (SAR) from urban areas under complex scenarios, an unsupervised target detection method that combines the superpixel segmentation and the Wishart classifier is proposed. Firstly, the buildings are detected based on the different polarimetric scattering characteristics of ground objects. Then, the morphological information of the target is obtained by the Wishart classifier without buildings and the superpixel segmentation. After that, the center points of the target are obtained by the Wishart classifier with buildings. Finally, the region growing procedure is used to fuse the information obtained by above-mentio ned classifiers to complete the target detection task. Experimental results implemented on X-band airborne polarimetric SAR data illustrate that the proposed method can not only distinguish and locate dense targets, but also keep the shape of targets. Compared with traditional methods, the performance of target detection and false alarm discrimiantion is improved.

Key words: vehicle detection, superpixel segmentation, polarimetric synthetic aperture radar(SAR), Wishart classifier

CLC Number:

TP751.1

Xiaokang DAI, Junjun YIN, Jian YANG. Vehicle detection based on Wishart distance and superpixel in polarimetric SAR image[J]. Systems Engineering and Electronics, 2021, 43(10): 2766-2774.

Figures/Tables 21

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Vehicle detection based on Wishart distance and superpixel in polarimetric SAR image

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