2 January 2018 Local region-based level set approach for fast synthetic aperture radar image segmentation
Author Affiliations +
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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Qingxia Meng, Qingxia Meng, Xianbin Wen, Xianbin Wen, Liming Yuan, Liming Yuan, Jiaxing Liu, Jiaxing Liu, Haixia Xu, Haixia Xu, } "Local region-based level set approach for fast synthetic aperture radar image segmentation," Journal of Applied Remote Sensing 12(1), 015002 (2 January 2018). https://doi.org/10.1117/1.JRS.12.015002 . Submission: Received: 5 May 2017; Accepted: 7 December 2017
Received: 5 May 2017; Accepted: 7 December 2017; Published: 2 January 2018
JOURNAL ARTICLE
8 PAGES


SHARE
Back to Top