Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23236
Title: Extracting man-made objects from high spatial resolution remote sensing images via fast level set evolutions
Authors: Li, Z
Shi, W 
Wang, Q
Miao, Z
Keywords: Airport runway extraction
Building roof extraction
Chan-Vese model
High spatial resolution
Level set evolution (LSE)
Man-made object extraction
Road network extraction
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on geoscience and remote sensing, 2015, v. 53, no. 2, 6863659, p. 883-899 How to cite?
Journal: IEEE transactions on geoscience and remote sensing 
Abstract: Object extraction from remote sensing images has long been an intensive research topic in the field of surveying and mapping. Most past methods are devoted to handling just one type of object, and little attention has been paid to improving the computational efficiency. In recent years, level set evolution (LSE) has been shown to be very promising for object extraction in the field of image processing because it can handle topological changes automatically while achieving high accuracy. However, the application of state-of-the-art LSEs is compromised by laborious parameter tuning and expensive computation. In this paper, we proposed two fast LSEs for man-made object extraction from high spatial resolution remote sensing images. We replaced the traditional mean curvature-based regularization term by a Gaussian kernel, and it is mathematically sound to do that. Thus, we can use a larger time step in the numerical scheme to expedite the proposed LSEs. Compared with existing methods, the proposed LSEs are significantly faster. Most importantly, they involve much fewer parameters while achieving better performance. Their advantages over other state-of-the-art approaches have been verified by a range of experiments.
URI: http://hdl.handle.net/10397/23236
ISSN: 0196-2892 (print)
1558-0644 (online)
DOI: 10.1109/TGRS.2014.2330341
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