Automatic power line extraction from high resolution remote sensing imagery based on an improved Radon transform. (January 2016)
- Record Type:
- Journal Article
- Title:
- Automatic power line extraction from high resolution remote sensing imagery based on an improved Radon transform. (January 2016)
- Main Title:
- Automatic power line extraction from high resolution remote sensing imagery based on an improved Radon transform
- Authors:
- Chen, Yunping
Li, Yang
Zhang, Huixiong
Tong, Ling
Cao, Yongxing
Xue, Zhihang - Abstract:
- Abstract: In this paper, we propose a new algorithm for power line identification and extraction from high resolution remote sensing images. Theoretically, it is difficult to detect power lines in satellite images due to some characteristics, such as sub-pixel, weak target, discrete and the complicated background. To our knowledge, the problem of extraction of the power lines from satellite images is faced for the first time. An improved Radon transform, Cluster Radon Transform (CRT), was developed to extract linear feature from satellite image. Compared with conventional Radon transform, CRT can efficiently avoid false alarm. After that, a set of rules of power lines was abstracted to distinguish power lines from other linear feature, such as roads. The experimental results show that CRT not only has strong anti-noise capability to random noise, but also has strong anti-noise capability to system noise caused by non-linear feature. Furthermore, CRT also has the strong capability to detect short segment in an image. Finally, synthetic images and true images were used to verify the new approach. The achievement has potential to be applicable not only to the power line extraction, but also to other weak linear target detection. Highlights: An improved Radom transform, Cluster Radon Transform, for weak linear and short segment feature extraction is proposed. A distinguish algorithm for power line extraction are developed. A scheme for power line detection is constructed.
- Is Part Of:
- Pattern recognition. Volume 49(2016:Jan.)
- Journal:
- Pattern recognition
- Issue:
- Volume 49(2016:Jan.)
- Issue Display:
- Volume 49 (2016)
- Year:
- 2016
- Volume:
- 49
- Issue Sort Value:
- 2016-0049-0000-0000
- Page Start:
- 174
- Page End:
- 186
- Publication Date:
- 2016-01
- Subjects:
- High resolution -- Remote sensing image -- Power lines extraction -- Radon transform
Pattern perception -- Periodicals
Perception des structures -- Périodiques
Patroonherkenning
006.4 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00313203 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.patcog.2015.07.004 ↗
- Languages:
- English
- ISSNs:
- 0031-3203
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9064.xml