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Journal of Applied Remote Sensing

Local region-based level set approach for fast synthetic aperture radar image segmentation
Author(s): Qingxia Meng; Xianbin Wen; Liming Yuan; Jiaxing Liu; Haixia Xu
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Paper Abstract

Synthetic aperture radar (SAR) image segmentation is the key to SAR image automatic interpretation. However, speckle noise, intensity inhomogeneity, and irregular shaped objects with changing edge often make the SAR image segmentation very difficult, and existing algorithms have high computational complexity. We propose a region-based level set method using the local image intensity information. To represent the statistical characteristics of speckle noise, we first use a gamma statistical distribution to model every segmented SAR image. We then apply a modified region mean estimation formula to efficiently segment SAR images with inhomogeneity. Finally, Gaussian filtering is employed to regularize the level set function, which can avoid reinitialization. The experimental results on synthetic and real-world SAR images demonstrate that the proposed method has less computation cost, faster convergence rate, and more accurate segmentation results.

Paper Details

Date Published: 2 January 2018
PDF: 8 pages
J. Appl. Rem. Sens. 12(1) 015002 doi: 10.1117/1.JRS.12.015002
Published in: Journal of Applied Remote Sensing Volume 12, Issue 1
Show Author Affiliations
Qingxia Meng, Tianjin Univ. (China)
Xianbin Wen, Tianjin Univ. (China)
Tianjin Univ. of Technology (China)
Liming Yuan, Tianjin Univ. of Technology (China)
Jiaxing Liu, Tianjin Univ. of Technology (China)
Haixia Xu, Tianjin Univ. of Technology (China)


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