Automatic Bridge Recognition Method in High Resolution PolSAR Images Based on CFAR Detector

CHANG Yonglei; YANG Jie; LI Pingxiang; ZHAO Lingli; YU Jie

doi:10.13203/j.whugis20140828

Volume 42 Issue 6

Jun. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(6): 762-767. > DOI: 10.13203/j.whugis20140828

CHANG Yonglei, YANG Jie, LI Pingxiang, ZHAO Lingli, YU Jie. Automatic Bridge Recognition Method in High Resolution PolSAR Images Based on CFAR Detector[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 762-767. DOI: 10.13203/j.whugis20140828

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Automatic Bridge Recognition Method in High Resolution PolSAR Images Based on CFAR Detector

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3.
College of Resources, Environment and Tourism, Capital Normal University, Beijing 100038, China

Funds:

The Commonweal Surveying Project 201412002

the National Natural Science Foundation of China 91438203

the National Natural Science Foundation of China 61371199

the Project of Yantai Oil Spill Response Technical Center of China MSA

the Project of Beijing Key Laboratory of Urban Spatial Information Engineering 2014204

the Project of National Administration of Surveying, Mapping and Geo-Information through the Key Laboratory of Geo-Informatics 201406

More Information

Author Bio:
CHANG Yonglei, PhD candidate, specializes in PolSAR image processing. E-mail: chang_yonglei@163.com
Corresponding author:
YANG Jie, PhD, professor., E-mail: yangj@whu.edu.cn
Received Date: October 08, 2015
Published Date: June 04, 2017

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Abstract

Abstract

The automatic recognition of bridges has both civil and military significance. However, in complicated cases when the image resolution is at the decimeter scale. the bridge scenes are messy and the targets small, and automatic recognition will become quite complicated. Thus, we proposed a novel algorithm based on the analysis of the statistical distribution and features of bridge targets in high-resolution SAR images. A CFAR detector locates potential bridge targets based on the Weibull distribution. Scene areas of bridges are extracted and false alarms are removed by utilizing the features of bridges with the help of Hough transformation. Domestic airborne polarimetric SAR data and AIRSAR data illustrate the effectiveness of this method. Results indicate that this algorithm recognizes bridges in complicated cases with high adaptability.
- PolSAR,
- high resolution,
- recognition of bridge target,
- CFAR,
- Weibull distribution

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[1]	杨文, 陈嘉宇, 孙洪, 等.基于SAR图像的点状目标检测方法研究[J].电波科学学报, 2004, 19(3): 362-366 http://www.cnki.com.cn/Article/CJFDTOTAL-DBKX200403022.htm Yang Wen, Chen Jiayu, Sun Hong, et al. Study on Point Targets Detection in SAR Images[J]. Chinese Journal of Radio Science, 2004, 19(3): 362-366 http://www.cnki.com.cn/Article/CJFDTOTAL-DBKX200403022.htm
[2]	Peng Z, Liu S, Tian G, et al. Bridge Detection and Recognition in Remote Sensing SAR Images Using Pulse Coupled Neural Networks[M]//Advances in Neural Network Research and Applications. Berlin, Germany: Springer, 2010
[3]	Zhang X, Song J, Yi Z, et al. Extraction and Recognition of Bridges over Water in High Resolution SAR Image[M]//Advances in Multimedia, Software Engineering and Computing. Berlin, Germany: Springer, 2011
[4]	Yin D, Zhang R. Research on the Recognition Method of Bridge Target in SAR Image[C]. IEEE International Conference on Computer and Information Technology, Bradford, UK, 2010
[5]	戴光照, 张荣.高分辨率SAR图像中的桥梁识别方法研究[J].遥感学报, 2007, 11(2): 177-184 doi: 10.11834/jrs.20070224 Dai Guangzhao, Zhang Rong. A Study of Bridge Recognition in High Resolution SAR Images[J]. Journal of Remote Sensing, 2007, 11(2): 177-184 doi: 10.11834/jrs.20070224
[6]	Zheng N, Zheng W, Xu Z L, et al. Bridge Target Detection in SAR Images Based on Texture Feature[J]. Applied Mechanics and Materials, 2013, 347(1): 3 634-3 638 https://www.scientific.net/AMM.347-350.3634
[7]	董银文, 苑秉成, 石钊铭.基于区域特征的航拍图像水上桥梁自动识别[J].武汉大学学报·信息科学版, 2013, 38(9): 1 073-1 077 http://ch.whu.edu.cn/CN/abstract/abstract2748.shtml Dong Yinwen, Yuan Bingcheng, Shi Zhaoming. Automatic Recognition of Bridge Above Water in Aerial Images Based on Regional Feature[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1 073-1 077 http://ch.whu.edu.cn/CN/abstract/abstract2748.shtml
[8]	李云帆, 马洪超, 邬建伟, 等.顾及地形特征的机载LiDAR数据桥梁提取算法研究[J].武汉大学学报·信息科学版, 2011, 36(5): 552-555 http://ch.whu.edu.cn/CN/abstract/abstract535.shtml Li Yunfan, Ma Hongchao, Wu Jianwei, et al. Extracting Bridges from Airborne LiDAR Data Based on Terrain Features[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5): 552-555 http://ch.whu.edu.cn/CN/abstract/abstract535.shtml
[9]	Di Bisceglie M, Galdi C. CFAR Detection of Extended Objects in High Resolution SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 833-843 doi: 10.1109/TGRS.2004.843190
[10]	Jung C H, Song W Y, Rho S H, et al. Double-Step Fast CFAR Scheme for Multiple Target Detection in High Resolution SAR Images[C]. IEEE National Radar Conference, Washington D C, USA, 2010
[11]	Cohen A C. Maximum Likelihood Estimation in the Weibull Distribution Based on Complete and Censored Samples[J]. Technometrics, 1965, 7(4): 579-588 doi: 10.1080/00401706.1965.10490300
[12]	Shi L. The Low Backscattering Targets Classification in Urban Areas[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012(1-7): 171-176 https://www.researchgate.net/publication/274676969_THE_LOW_BACKSCATTERING_TARGETS_CLASSIFICATION_IN_URBAN_AREAS
[13]	Cloude S R, Pottier E. An Entropy Based Classification Scheme for Land Applications of Polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 68-78 doi: 10.1109/36.551935
[14]	Marino A, Cloude S R, Woodhouse I H. Detecting Depolarized Targets Using a New Geometrical Perturbation Filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10): 3 787-3 799 doi: 10.1109/TGRS.2012.2185703
[15]	Ballard D H. Generalizing the Hough Transform to Detect Arbitrary Shapes[J]. Pattern Recognition, 1981, 13(2): 111-122 doi: 10.1016/0031-3203(81)90009-1

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