复杂场景下的极化SAR图像机场跑道检测

提出一种基于极化分解分类与结构特征相结合的复杂场景全极化SAR图像机场跑道检测方法。首先利用先验信息粗选图像中各类样本目标进行H/α分解提取图像中各类训练样本，然后根据极化SAR图像的统计特性，利用贝叶斯分类器对图像进行分类，提取图像中机场跑道疑似区域，再结合机场跑道的五种结构特征用二叉树法进行判别，最终确定机场跑道区域。利用美国UAVSAR系统采集的多组全极化实测数据对算法进行实验，结果表明，该算法能够有效地检测出跑道，且检测的跑道结构完整，轮廓清晰，虚警率低。 

Runways Detection in Complex Scenes of Polarimetric Synthetic Aperture Radar Image

In this paper, a new algorithm of runways detection based on polarimetric decomposition and structural characteristics in complex scenes of fully polarimetric synthetic aperture radar image is proposed. Firstly, training samples are obtained according to the priori of backward scattering mechanism of the terrain and H/α decomposition. Then runway suspected areas are achieved after Bayesian classification combining the statistical characteristic of covariance matrix and the training samples. Finally, binary decision tree is used to identify runways combining with five structural characteristics, to eliminate the suspected runway areas and to finalize the airport runway region. Multi-look fully polarimetric SAR datas acquired by U.S.UAVSAR systems is used to verify the new algorithm. The experimental results show that the novel method can detect runways effectively. Besides, the detected runways own a intact structure, clear outlines and low false alarm rate.