适用于高分辨SAR图像的全局稳态最小水平集分割方法

冯籍澜; 曹宗杰; 皮亦鸣

doi:10.3724/SP.J.1146.2009.01622

适用于高分辨SAR图像的全局稳态最小水平集分割方法

doi: 10.3724/SP.J.1146.2009.01622

基金项目:

国家自然科学基金(60802065, 60772143)资助课题

计量
- 文章访问数: 4251
- HTML全文浏览量: 124
- PDF下载量: 844
- 被引次数: 0
出版历程
- 收稿日期: 2009-12-22
- 修回日期: 2010-04-21
- 刊出日期: 2010-11-19

A Global Stationary Minimum Level Set Segmentation Method for High-resolution SAR Images

摘要

摘要: 该文针对高分辨率SAR图像的分割问题提出了一种新的快速的水平集方法。该方法基于G0分布能够同时描述高分辨率和中低分辨率条件下的SAR图像统计特性，通过水平集方法求解能量泛函最小化实现SAR图像的分割。由于能量泛函被设计为具有全局稳态最小值，使得该方法具有较好的全局分割能力和比较快的分割速度，从而增强了该方法的实用性。利用模拟和真实SAR图像上的分割实验验证了该方法的有效性。
- SAR图像处理 /
- 变分水平集方法 /
- 全局稳态最小 /
- G0分布
Abstract: A new and fast level set method for segmentation of high-resolution SAR images into statistical homogenous areas is proposed. This approach is based on the G0 statistical model which can describe high-resolution SAR images as well as low-resolution SAR images. And the segmentation is obtained by minimizing energy function with level set methods. As the energy functional is designed to have global stationary minimum, a global and fast segmentation technique can be obtained, thus the practicality of the proposed approach is enhanced. The performance of the algorithm is verified with experiments based on both synthetic and real SAR images.
- SAR image processing /
- Level set methods /
- Global minimum /
- G0 distribution

HTML全文

参考文献(1)

Chan T F and Vese L A. Active contours without edges [J].IEEE Transactions on Image Processing.2001, 10(2):266-277[2]Lee S H and Seo J K. Level set-based bimodal segmentation with stationary global minimum [J].IEEE Transactions on Image Processing.2006, 15(9):2843-2852[3]Chung Ginmo and Vese L A. Image segmantation using a multilayer level set approach [J].Computing and Visualization in Science.2009, 12(6):267-285[4]Zhu S C and Yullie A. Region competition: unifying snakes, region growing, and Bayes/MDL for multibad image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 16(9): 884-900.[5]Ayed I B, Mitiche A, and Belhadj Z. Multiregion level- set partition of synthetic aperture radar image [J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2005, 27(5):793-800[6]Ayed I B, Hennane N, and Mitiche A. Unsupervised variational image segmentation/classification using a Weibull observation model [J].IEEE Transactions on Image Processing.2006, 15(11):3431-3439[7]曹宗杰，闵锐，庞伶俐等. 基于统计模型的变分水平集SAR图像分割方法[J].电子与信息学报.2008, 30(12):2862-2866浏览Cao Zong-jie, Min Rui, and Pand Ling-li, et al.. A variational level set SAR image segmentation approach based on statistical model[J].Journal of Electronics Information Technology.2008, 30(12):2862-2866[8]高贵，鲁敏，黄纪军等. 高分辨率SAR图像中杂波的统计特性分析[J]. 信号处理，2008, 24(4): 648-654.Gao Gui, Lu Min, and Huang Ji-jun, et al.. Statistical analysis of clutter in high-resolution SAR images. Signal Processing, 2008, 24(4): 648-654.[9]Frery A C, Muller H J, and Freitas, et al.. A model for extremely heterogeneous clutter [J].IEEE Transactions on Geosciences and Remote Sensing.1997, 35(3):648-659[10]Galland F, Nicolas J M, and Sporouche H, et al.. Unsupervise synthetic aperture radar image segmentation using Fisher distributions[J].IEEE Transactions Geoscience and Remote Sensing.2009, 47(8):2966-2972[11]Bresson X, Wsedoglu S, and Vandergheynst P, et al.. Fast global minimization of the active contours/snake model[J].Journal of Mathimatic Imaging Vision.2007, 28(1):151-167

施引文献

资源附件(0)

访问统计