Method for extracting specific parallel straight lines

Lingli Zhao; Jianjun Zhu; Shuai Liu; Li Ma

doi:10.1117/12.750196

15 November 2007 Method for extracting specific parallel straight lines

Lingli Zhao, Jianjun Zhu, Shuai Liu, Li Ma

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67864T (2007) https://doi.org/10.1117/12.750196
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Targets based on linear feature often need to be extracted and identified with high precision in the application of photogrammetry. Linear features such as hydrological object boundaries, roads and the boundary of other man made objects are very important for geospatial information extraction and analysis from remotely sensed imagery. This paper deals with subpixel accuracy extraction of linear features, especially specific parallel straight lines. There are many algorithms for localization, such as Gray moment operator, Hough transformation algorithm Forstner operator and so on, just only adopting a single algorithm, the precision of localization is low, and the calculation is complicated. So a new mixed method is proposed in the paper and the procedure can be divided into two steps. Firstly, the rough location of the parallel straight lines was extracted with Hough transformation algorithm. We could get the initial value of parallel straight line in this step. Secondly, straight linear fitting based on Gray moment operator for edge location was adopted to extract the straight lines with high precision. The experimental results indicate that the mixed method for subpixel localization locates the target very validly, and some practicable conclusions are received.

Citation Download Citation

Lingli Zhao, Jianjun Zhu, Shuai Liu, and Li Ma "Method for extracting specific parallel straight lines", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67864T (15 November 2007); https://doi.org/10.1117/12.750196

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