基于差异度迭代的极化SAR图像机场跑道检测算法

机场在军事和交通运输领域都有很重要的作用,对它的自动检测具有重大意义。本文提出了一种利用极化SAR(Polarimetric Synthetic Aperture Radar)图像检测机场跑道的方法。首先,利用SLIC(Simple linear Iterative Clustering)算法对极化SAR图像进行超像素分割;然后利用新三分量分解和极化散射熵对图像进行粗分类,再利用改进的K均值聚类结合差异度迭代的方法完成精细分类,最后利用跑道的散射特性和几何结构特征从分类结果中提取完整的机场区域。本文采用极化SAR数据进行实验检测,结果表明该方法能有效的检测出机场跑道,并且保持结构完整,边缘细节清晰,虚警率低。 

Airport Runways Detection Algorithm Based on Difference Degree Iteration in PolSAR Image

Airports are crucial infrastructures in both civil and military transportation systems. Automatic detection of airports then has been attracting significant research interests in the remote sensing areas. A method for detecting airport runways in a Polarimetric Synthetic Aperture Radar(PolSAR) image using difference degree iteration is proposed in this paper. Superpixels are generated by Simple Linear Iterative Clustering(SLIC) algorithm which is applied to the PolSAR image in the segmentation step, followed by a coarse classification according to the three-component decomposition feature and scattering entropy. The fine classification, combining the enhanced K-means clustering method with the difference degree iteration, is then applied. The extraction of runways and the complete airport areas from the classification results can then be achieved by exploiting both of the scattering and geometric features. The UAVSAR data set is used for verifying the performance of the proposed method. And the results suggest that the airport runway can be detected effectively by the proposed method, while maintaining the detailed structures intact with clear edges. A low false alarm rate is also exhibited during the numerical experiments.