利用倒数灰度熵和改进Chan-Vese模型进行SAR河流图像分割

吴诗婳; 吴一全; 周建江; 孟天亮

doi:10.11947/j.AGCS.2015.20140519

测绘学报 >

2015 , Vol. 44 >Issue 11: 1255 - 1262

DOI: https://doi.org/10.11947/j.AGCS.2015.20140519

摄影测量学与遥感

利用倒数灰度熵和改进Chan-Vese模型进行SAR河流图像分割

吴诗婳 ,
吴一全 ,
周建江 ,
孟天亮

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1. 南京航空航天大学电子信息工程学院, 江苏南京 201116;
2. 长江水利委员会长江科学院武汉市智慧流域工程技术研究中心, 湖北武汉 430010;
3. 黄河水利委员会黄河水利科学研究院水利部黄河泥沙重点实验室, 河南郑州 450003;
4. 南京水利科学研究院港口航道泥沙工程交通行业重点实验室, 江苏南京 210024;
5. 哈尔滨工业大学城市水资源与水环境国家重点实验室, 黑龙江哈尔滨 150090

吴诗婳(1992-),女,硕士生,研究方向为遥感图像处理。E-mail:wshimage@163.com

收稿日期: 2014-10-09

修回日期: 2015-03-23

网络出版日期: 2015-11-25

基金资助

国家自然科学基金(60872065);长江科学院开放基金(CKWV2013225/KY);水利部黄河泥沙重点实验室开放基金(2014006);港口航道泥沙工程交通行业重点实验室开放基金;城市水资源与水环境国家重点实验室开放基金(LYPK201304);中央高校基本科研业务费专项资金;江苏省普通高校研究生科研创新计划(SJLX15_0116);江苏高校优势学科建设工程

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SAR River Image Segmentation Based on Reciprocal Gray Entropy and Improved Chan-Vese Model

WU Shihua ,
WU Yiquan ,
ZHOU Jianjiang ,
MENG Tianliang

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1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 201116, China;
2. Engineering Technology Research Center of Wuhan Intelligent Basin, Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China;
3. Key Laboratory of the Yellow River Sediment of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Yellow River Water Resources Commission, Zhengzhou 450003, China;
4. Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport, Nanjing Hydraulic Research Institute, Nanjing 210024, China;
5. State Key Laboratory of Urban Water Resources and Environment, Harbin Institute of Technology, Harbin 150090, ChinaAbstract

Received date: 2014-10-09

Revised date: 2015-03-23

Online published: 2015-11-25

Supported by

The National Natural Science Foundation of China(No. 60872065) Open Foundation of Changjiang River Scientific Research Institute(No. CKWV2013225/KY);Open Foundation of the Key Laboratory of the Yellow River Sediment of Ministry of Water Resources(No. 2014006);Open Foundation of the Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport;Open Foundation of the State Key Laboratory of Urban Water Resources and Environment(No. LYPK201304);The Fundamental Research Funds for the Central University Funding of Jiangsu Innovation Program for Graduate Education(No.SJLX15_0116);The Priority Academic Program Development of Jiangsu Higher Education Institution

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摘要

为了进一步提高合成孔径雷达(SAR)图像中河流分割的精度和速度,提出了一种基于人工蜂群优化的倒数灰度熵多阈值选取与改进Chan-Vese(CV)模型相结合的分割方法。考虑SAR图像中河流目标和背景类内灰度的均匀性,提出了基于蜂群优化的倒数灰度熵多阈值选取方法,以此对河流图像进行粗分割;针对基本CV模型收敛速度低、对初始条件敏感的问题,利用图像边缘强度取代Dirac函数,将粗分割结果作为改进CV模型的初始条件,对河流图像进行细分割。大量试验结果表明,所提出的分割方法无须设置初始条件,运行速度快,分割精度高。

关键词： 河流检测; 合成孔径雷达图像分割; 多阈值选取; 倒数灰度熵; 人工蜂群优化; Chan-Vese模型

本文引用格式

吴诗婳 , 吴一全 , 周建江 , 孟天亮 . 利用倒数灰度熵和改进Chan-Vese模型进行SAR河流图像分割[J]. 测绘学报, 2015 , 44(11) : 1255 -1262 . DOI: 10.11947/j.AGCS.2015.20140519

Abstract

To further improve the accuracy and speed of river segmentation on synthetic aperture radar(SAR) images, a segmentation method is proposed, which is based on improved Chan-Vese(CV) model combining with reciprocal gray entropy multi-threshold selection optimized by artificial bee colony algorithm. Considering the uniformity of the gray level within river object cluster and background cluster, a coarse river image segmentation is made by using the multi-threshold selection algorithm based on reciprocal gray entropy and artificial bee colony optimization; Contrapose the low convergence speed and the sensitivity to initial conditions of basic CV model, the Dirac function is replaced with the image edge intensity and the coarse segmentation results serve as the initial condition of improved CV model which is utilized to make a fine segmentation for the river image. A large number of experimental results show that, the proposed segmentation method needs not set initial conditions and has high running speed as well as segmentation accuracy.

Key words： river detection; synthetic aperture radar image segmentation; multi-threshold selection; reciprocal gray entropy; artificial bee colony optimization; Chan-Vese(CV)model

参考文献

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