Local region-based level set approach for fast synthetic aperture radar image segmentation

Qingxia Meng; Xianbin Wen; Liming Yuan; Jiaxing Liu; Haixia Xu

doi:10.1117/1.JRS.12.015002

2 January 2018 Local region-based level set approach for fast synthetic aperture radar image segmentation

Qingxia Meng, Xianbin Wen, Liming Yuan, Jiaxing Liu, Haixia Xu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 015002 (January 2018). https://doi.org/10.1117/1.JRS.12.015002

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.

Citation Download Citation

Qingxia Meng, Xianbin Wen, Liming Yuan, Jiaxing Liu, and 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

Received: 5 May 2017; Accepted: 7 December 2017; Published: 2 January 2018

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