摘要
机场跑道边缘像素因具有类别模糊性而易产生误分类,为此提出一种基于鲁棒模糊聚类的PolSAR图像跑道区域检测方法。为避免模糊聚类对初始类中心敏感而陷入局部最优,首先利用极化分解理论对原始图像进行粗分割,得到合适的初始类中心。然后采用基于Wishart距离的鲁棒模糊聚类算法对PolSAR图像进行分类,并提取感兴趣区域(即疑似跑道区域)。最后利用跑道结构特征对感兴趣区域进行辨识,确定真实跑道位置。仿真结果表明,相比其它两种检测算法,该检测算法具有精度高、速度快、视觉效果好的特点。
The edge pixels of airport runway may be misclassified due to category ambiguity.Therefore,a runway area detection method for PolSAR image based on robust fuzzy C-means clustering is proposed.Firstly,in order to avoid the sensibility to initial centers and local optimum occurring in fuzzy C-means clustering,the polarization decomposition theory is utilized to segment the original image and obtain appropriate initial centers.Then the regions of interest(i.e.suspected runway areas)are extracted from the classification results,which are obtained by the robust fuzzy C-means clustering method based on Wishart distance.Finally,structural features of runway are used to identify the extracted areas and finalize the real runway area.Smiulation results show that the proposed method has higher accuracy,faster speed,and better visual effect comparing with the other two algorithms.
作者
程争
韩萍
韩绍程
CHENG Zheng;HAN Ping;HAN Shaocheng(Basic Experiment Center,CAUC,Tianjin 300300,China;College of Electronic Information and Automation,CAUC,Tianjin 300300,China)
出处
《中国民航大学学报》
CAS
2019年第5期30-34,共5页
Journal of Civil Aviation University of China
基金
国家自然科学基金项目(61571442)
天津市教委科研计划项目(2018KJ246)
中央高校基本科研业务费专项(3122018S008)
关键词
极化合成孔径雷达
跑道区域检测
模糊聚类
Wishart距离
结构特征
polarimetric synthetic aperture radar
runway area detection
fuzzy clustering
Wishart distance
structural characteristics
