Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19849
Title: An automatic road segmentation algorithm using one-class SVM
Authors: Zheng, S
Liu, J
Shi, W 
Zhu, G
Keywords: Image segmentation
One-class support vector machine (SVM)
Pixel-based method
Road extraction
Issue Date: 2006
Publisher: SPIE-International Society for Optical Engineering
Source: Proceedings of SPIE : the International Society for Optical Engineering, 2006, v. 6419, 64191B How to cite?
Journal: Proceedings of SPIE : the International Society for Optical Engineering 
Abstract: Automatic feature extraction for road information plays a central role in applications related to terrains. In this paper, we propose a new road extraction method using the one-class support vector machine (SVM). For a manually segmented seed road region, only a part of pixels are really road, some pixels locating on the sideway, shadows of the building, and the cars etc., are not really road pixels. The one-class SVM is used to estimate a decision function that takes the value +1 in a small feature region capturing most of the data points in the seed road area, and -1 elsewhere. Since the road pixels in the satellite image have the similar properties, such as the spectral feature in multi-spectral image, the novelty pixel is discriminated by the estimated decision function for road segmentation. Many computation experiments are undertaken on the IKONOS high resolution image. The results demonstrate that the proposed method is effective and has much higher computation efficiency than the standard pixel-based SVM classification method.
Description: Geoinformatics 2006: Remotely Sensed Data and Information, Wuhan, 28-29 October 2006
URI: http://hdl.handle.net/10397/19849
ISBN: 0819465283
9780819465283
ISSN: 0277-786X
EISSN: 1996-756X
DOI: 10.1117/12.713162
Appears in Collections:Conference Paper

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