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An enhanced multiphase Chan–Vese model for the remote sensing image segmentation
Author(s) -
Yi Xin,
Hu Yingjie,
Jia Zhenhong,
Wang Liejun,
Yang Jie,
Kasabov Nikola
Publication year - 2013
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3185
Subject(s) - initialization , computer science , segmentation , image segmentation , level set (data structures) , artificial intelligence , computation , image (mathematics) , terrain , level set method , set (abstract data type) , computer vision , pattern recognition (psychology) , remote sensing , algorithm , geology , geography , cartography , programming language
SUMMARY The level set method has been widely used in image segmentation; however, the complexity of the computation has restricted its application field. Also, it is a big challenge to segment remote sensing image mainly because of the complex terrain. In this paper, an enhanced multiphase phase level set method based on the Chan–Vese (C‐V) model is proposed for segmenting remote sensing images. Compared with the C‐V model, two main contributions of the proposed model mainly include the following: First, we introduce a new strategy of initialization in which the contours of the first k biggest connected regions are extracted as the initial curves (k is the number of level set functions); Second, to increase the accuracy, a morphological gradient component is added to the original intensity image. To investigate the effectiveness and efficiency of the proposed model, we have applied it to analyze different kinds of images, including synthetic, real, and remote sensing images. The experimental results have shown that our method is able to achieve better segmentation with less computational consumption compared with the traditional multiphase C‐V model and local and global intensity fitting model. Copyright © 2013 John Wiley & Sons, Ltd.