基于Wishart距离和超像素的极化SAR图像车辆检测

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

针对极化合成孔径雷达(synthetic aperture radar,SAR)在城市区域复杂地物条件下的密集车辆目标检测问题,提出了一种结合超像素分割和Wishart分类器的非监督目标检测方法。首先,根据不同地物的极化散射特征检测出建筑物。然后,利用不包含建筑物的Wishart分类器和超像素分割获得目标的形态信息。接着,利用包含建筑物的Wishart分类器获得目标中心点。最后,通过区域生长对二者进行信息融合并完成目标检测任务。基于X波段的机载极化SAR数据表明,所提算法不仅可以对密集目标进行区分和定位,并且目标形态保持完整;相比于传统方法,目标检测与虚警鉴别性能得到较大提升。

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-mentioned 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.