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 
Issue Date: 2014
Source: IEEE geoscience and remote sensing letters, 2014, v. 11, no. 4, 6590020, p. 743-747
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.
Keywords: Building roof recognition
Chan-Vese (CV) model
Fast level set algorithm
High spatial resolution
Panchromatic image
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE geoscience and remote sensing letters 
ISSN: 1545-598X
DOI: 10.1109/LGRS.2013.2278342
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