|Table of Contents|

A fast detection method of airport runway area based on region segmentation and Wishart classifier(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2020年03期
Page:
225-236
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
A fast detection method of airport runway area based on region segmentation and Wishart classifier
Author(s):
HAN Ping1; 2;  CHENG Zheng2; 3;  WAN Yi-shuang1; 2;  HAN Bin-bin1; 2;  HAN Shao-cheng3
(1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China; 2. Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China; 3. Basic Experiment Center, Civil Aviation University of China, Tianjin 300300, China)
Keywords:
traffic information;  airport runway area detection;  polarimetric synthetic aperture radar;  simple liner iterative clustering;  Wishart distance measurement factor;  structural characteristics
PACS:
V249.3
DOI:
10.19818/j.cnki.1671-1637.2020.03.021
Abstract:
A fast detection method of airport runway area using polarimetric synthetic aperture radar images was proposed based on region segmentation and Wishart classifier. A simple liner iterative clustering algorithm was utilized to partition polarimetric synthetic aperture radar image into many super-pixels, and these super-pixels were regarded as basic units for subsequent classification processing. An optimized distance measurement method was adopted to assign appropriate category labels for the super-pixels, greatly solving the problem of large redundant computation of traditional Wishart distance measurement factor. The polarization scattering characteristics of the pixels in airport runway area were analyzed, and the interest regions were extracted from the classification results using the weak scattering characteristic of airport runway area. The structural characteristics of airport runway were used to select and identify the extracted interest regions, thereby the accurate location of airport runway region was determined. The validity of the proposed algorithm was tested by the measured datafrom polarimetric synthetic aperture radar, and the detection results were compared with the traditional pixel-based detection results. Experimental result shows that in the large complicated scenes, the algorithm can detect the runway area fast and effectively, and the detected runway has clear outline and relatively complete structure. Using the simple linear iterative clustering algorithm to preprocess the images reduces the complexity of subsequent processing significantly. Based on the experimental data in the Gulf of Mexico, the processing unit number of Wishart classifier is only 1.0% and 2.4% of the numbers of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively, and the whole detection time is 9.9% and 27.1% of those of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively. Based on the experimental data in the Big Island, the processing unit number of Wishart classifier is only 1.0% and 2.6% of those of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively, and the whole detection time is 14.0% and 31.8% of those of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively. Thus, the real-time performance of the proposed detection method is superior to that of the pixel-based detection method. 6 tabs, 12 figs, 30 refs.

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Last Update: 2020-07-10