Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30214
Title: A fast level set algorithm for building roof recognition from high spatial resolution panchromatic images
Authors: Li, Z
Liu, Z 
Shi, W 
Keywords: Building roof recognition
Chan-Vese (CV) model
Fast level set algorithm
High spatial resolution
Panchromatic image
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE geoscience and remote sensing letters, 2014, v. 11, no. 4, 6590020, p. 743-747 How to cite?
Journal: IEEE geoscience and remote sensing letters 
Abstract: Traditional level set methods usually require repeated tuning of parameters, which is quite laborious and thus limits their applications. In order to simplify the parameter setting, this letter presents a fast level set algorithm that is a further extension of the original Chan-Vese model. For computational efficiency, we start by initializing the level set function in our algorithm as a binary step function rather than the often used signed distance function. Then, we eliminate the curvature-based regularizing term that is commonly used in traditional models. Thus, we can use a relatively larger time step in the numerical scheme to expedite our model. Furthermore, to keep the evolving level curves smooth, we introduce a Gaussian kernel into our algorithm to convolve the updated level set function directly. Finally, compared with other existing popular algorithms in an experiment of recognizing building roofs from high spatial resolution panchromatic images, the proposed model is much more computationally efficient while object recognition performance is comparable to other popular models.
URI: http://hdl.handle.net/10397/30214
ISSN: 1545-598X
DOI: 10.1109/LGRS.2013.2278342
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

9
Last Week
0
Last month
0
Citations as of Jul 21, 2017

WEB OF SCIENCETM
Citations

7
Last Week
0
Last month
1
Citations as of Aug 15, 2017

Page view(s)

35
Last Week
2
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.