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

被引：0
|
作者：
Chang Y. [1 ]
Yang J. [1 ]
Li P. [1 ]
Zhao L. [2 ]
Yu J. [3 ]
机构：
[1] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan
[2] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan
[3] College of Resources, Environment and Tourism, Capital Normal University, Beijing
来源：
Yang, Jie (yangj@whu.edu.cn) | 1600年 / Editorial Board of Medical Journal of Wuhan University卷 / 42期
基金：
中国国家自然科学基金;
关键词：
CFAR; High resolution; PolSAR; Recognition of bridge target; Weibull distribution;
D O I：
10.13203/j.whugis20140828
中图分类号：
学科分类号：
摘要：
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. © 2017, Research and Development Office of Wuhan University. All right reserved.
引用
收藏
页码：762 / 767
页数：5
相关论文
共 15 条
  • [1] Yang W., Chen J., Sun H., Et al., Study on Point Targets Detection in SAR Images, Chinese Journal of Radio Science, 19, 3, pp. 362-366, (2004)
  • [2] Peng Z., Liu S., Tian G., Et al., Bridge Detection and Recognition in Remote Sensing SAR Images Using Pulse Coupled Neural Networks, Advances in Neural Network Research and Applications, (2010)
  • [3] Zhang X., Song J., Yi Z., Et al., Extraction and Recognition of Bridges over Water in High Resolution SAR Image, Advances in Multimedia, Software Engineering and Computing, (2011)
  • [4] Yin D., Zhang R., Research on the Recognition Method of Bridge Target in SAR Image, IEEE International Conference on Computer and Information Technology, (2010)
  • [5] Dai G., Zhang R., A Study of Bridge Recognition in High Resolution SAR Images, Journal of Remote Sensing, 11, 2, pp. 177-184, (2007)
  • [6] Zheng N., Zheng W., Xu Z.L., Et al., Bridge Target Detection in SAR Images Based on Texture Feature, Applied Mechanics and Materials, 347, 1, pp. 3634-3638, (2013)
  • [7] Dong Y., Yuan B., Shi Z., Automatic Recognition of Bridge Above Water in Aerial Images Based on Regional Feature, Geomatics and Information Science of Wuhan University, 38, 9, pp. 1073-1077, (2013)
  • [8] Li Y., Ma H., Wu J., Et al., Extracting Bridges from Airborne LiDAR Data Based on Terrain Features, Geomatics and Information Science of Wuhan University, 36, 5, pp. 552-555, (2011)
  • [9] Di Bisceglie M., Galdi C., CFAR Detection of Extended Objects in High Resolution SAR Images, IEEE Transactions on Geoscience and Remote Sensing, 43, 4, pp. 833-843, (2005)
  • [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, IEEE National Radar Conference, (2010)
12