基于多级分类的PolSAR图像机场跑道检测

提出一种复杂场景极化合成孔径雷达（polarimetric synthetic aperture radar， PolSAR）图像机场跑道多级分类检测方法。首先利用先验信息进行h/q第一级分类，得到图像中各类训练样本模板；然后利用PolSAR图像极化相干矩阵的统计特性进行第二级分类；再根据跑道的弱回波特性，利用极化总功率检测器完成第三级分类，提取图像中疑似机场跑道区域;最后根据机场跑道的尺寸及结构特征进行判别，确定机场跑道区域。采用美国UAVSAR机载系统获取的多组实测数据对算法进行验证，并与现有的两种方法进行比较，结果表明，本文算法能有效地检测出跑道，并保持跑道结构完整、轮廓清晰且虚警率低。

PolSAR image runways detection based on multi-stage classification

A new effective algorithm for complex scenes of polarimetric synthetic aperture radar (PolSAR) image runway detection based on multi-stage classification is developed. Firstly, training sample templates are selected according to h/q classification with the priori of backward scattering mechanism of the terrain. Then, the statistical characteristic of polarized coherent matrix of PolSAR images are used in the second stage classification. Thirdly, according to weak backscattering characteristic of the runways, the third stage classification is done by using the span detector. Finally, the airport runway region is identified with the size and structural characteristics of the runways. The datasets obtained by the U. S. UAVSAR airborne systems are used to do the experiments. Results show that comparing with the existing two methods, the new algorithm can detect the runways effectively with intact structures, clear outlines and low false alarm rates.