一种基于概率Hough变换的遥感图像中线目标检测新方法

doi:10.7517/j.issn.1674-0475.2017.02.162

影像科学与光化学 ›› 2017, Vol. 35 ›› Issue (2): 162-167.DOI: 10.7517/j.issn.1674-0475.2017.02.162

一种基于概率Hough变换的遥感图像中线目标检测新方法

巩学美¹, 高昆¹, 王研¹, 林娟¹, 朱建峰²

1. 北京理工大学光电成像技术与系统教育部重点实验室, 北京 100081;
2. 北京理工大学深圳研究院, 广东深圳 518057

收稿日期:2017-01-20 修回日期:2017-02-16 出版日期:2017-03-15 发布日期:2017-03-15
通讯作者: 高昆
基金资助:
北京自然科学基金项目（4152045）资助

A Novel Linear Target Detection Method Based on Improved Probability Hough Transform in Remote Sensing Imagery

GONG Xuemei¹, GAO Kun¹, WANG Yan¹, LIN Juan¹, ZHU Jianfeng²

1. Key Laboratory of Photoelectronic Imaging Technology and System(Beijing Institute of Technology), Ministry of Education, Beijing 100081, P. R. China;
2. Shenzhen Research Institute, Beijing Institute of Technology, Shenzhen 518057, Guangdong, P. R. China

Received:2017-01-20 Revised:2017-02-16 Online:2017-03-15 Published:2017-03-15

摘要/Abstract

摘要：

线状目标检测是遥感图像中人造目标识别的重要环节，其技术关键是如何有效地从背景中提取目标的长直边缘。Hough变换是从图像中检测直线的经典方法，但遥感影像的画幅大、背景复杂，标准的Hough变换会检测出大量短直线而影响对有用目标的检测效果。本文提出了一种改进的概率Hough变换方法，首先对原始遥感影像分块，利用Canny算子检测出每个子图像的边缘，再通过Hough变换检测出每个子图中的直线，并对其中的短直线聚类分组以排除干扰，最后再从剩下线段中提取并筛选出平行线以完成长直线目标检测。实验表明，这种方法对复杂背景下的遥感影像中的线状目标有良好的检测效果。

关键词: 概率Hough变换, 线性目标检测, 遥感

Abstract:

Linear target detection is one of the important courses in the artificial target recognition processing from the remote sensing imagery. The internal key technology is how to extract the long straight edges of the target from the background efficiently. Hough Transform is one of the classic methods for line detection. In view of the large scale and complex background in one remote sensing image, Standard Hough Transform (SHT) may generate too many short lines to distinguish the useful long linear targets. An improved method based on probabilistic Hough Transform (PHT) is proposed. Firstly, it divides the primary remote sensing image into sub-blocks and Canny operator is applied to detect edges inside each blocks. Then SHT is used to detect short lines and cluster them into groups to avoid most of the interfering lines. Finally, the parallel line pairs are extracted and filtered from the rest lines to confirm the long linear targets. Experiments show that the novel method can detect the linear target efficiently from the complex background.

Key words: probability Hough transform, linear target detection, remote sensing

巩学美, 高昆, 王研, 林娟, 朱建峰. 一种基于概率Hough变换的遥感图像中线目标检测新方法[J]. 影像科学与光化学, 2017, 35(2): 162-167.

GONG Xuemei, GAO Kun, WANG Yan, LIN Juan, ZHU Jianfeng. A Novel Linear Target Detection Method Based on Improved Probability Hough Transform in Remote Sensing Imagery[J]. Imaging Science and Photochemistry, 2017, 35(2): 162-167.

参考文献

[1] 宋晓静. 基于Hough变换目标检测问题研究[D]. 秦皇岛:燕山大学, 2015. Song X J. The study on target detection based on Hough transform[D].Qinhuangdao:Yanshan University, 2015.
[2] Duda R O, Hart P E. Use of the Hough transformation to detect lines and curves in pictures[J]. Communications of the ACM, 1972, 15(1):11-15.
[3] Hough P V C. Method and means for recognizing complex patterns[P]. US Patent, 3069654, 1962-12-18.
[4] Li Y, Cao Z R. Improved randomized Hough transform algorithm for circle detection[J]. Journal of Computer Applications, 2010, 6(1):174-176.
[5] Liu Y J, Lai R F, Rong W B, Sun L N. A fast center detecting method based on improved randomized Hough transform[J]. Nanotechnology & Precision Engineering, 2011, 9(4):298-304.
[6] Illingworth J, Kittler J. The adaptive hough transform[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1987, (5):690-698.
[7] Li H W, Lavin M A, Master R J L. Fast Hough transform:a hierarchical approach[J]. Computer Vision, Graphics, and Image Processing, 1986, 36(2-3):139-161.
[8] Atiquzzaman M. Multiresolution Hough transform-an efficient method of detecting patterns in images[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1992, 14(11):1090.
[9] Kiryati N, Eldar Y, Bruckstein A M. A probabilistic Hough transform[J]. Pattern Recognition, 1991, 24(4):303-316.
[10] Barni M, Cappellin V, Paoli A, Mecocci A. Unsupervised detection of straight lines through possibilistic clustering[C]. Image Processing, 1996. Proceedings, International Conference on. IEEE, 1996, 2:963-966.
[11] Canny J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, (6):679-698.
[12] Ommer B, Malik J. Multi-scale object detection by clustering lines[C]. Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009,484-491.
[13] Barinova O, Lempitsky V, Kohli P. On detection of multiple object instances using Hough transforms[J]. Computer Vision & Pattern Recognition, 2010, 34(9):2233-2240.
[14] Matas J, Kittler J, Galambos C. Robust detection of lines using the progressive probabilistic Hough transform[J]. Computer Vision & Image Understanding, 2000, 78(1):119-137.

一种基于概率Hough变换的遥感图像中线目标检测新方法

A Novel Linear Target Detection Method Based on Improved Probability Hough Transform in Remote Sensing Imagery

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