面向遥感图像水域分割的图像熵主动轮廓模型

王宇; 王田; 杨艺

doi:10.3788/OPE.20182603.0698

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面向遥感图像水域分割的图像熵主动轮廓模型

信息科学 | 更新时间：2020-07-05

- 面向遥感图像水域分割的图像熵主动轮廓模型
- Image entropy active contour models towards water area segmentation in remote sensing image
- 光学精密工程 2018年26卷第3期页码：698-707
- 作者机构：
  
  1.河南理工大学测绘与国土信息工程学院, 河南焦作 454000
  2.北京航空航天大学自动化科学与电气工程学院, 北京 100191
  3.河南理工大学电气工程与自动化学院, 河南焦作 454000
- 作者简介：
  
  [ "王宇(1978-), 女, 河南焦作人, 博士, 讲师。2002年于河南农业大学获学士学位, 2008年于河南理工大学获得硕士学位。主要从事遥感图像处理、土地利用规划等方面的研究工作。E-mail:wangyu@hpu.edu.cn" ]
  [ "王田(1987-), 男, 湖北孝感人, 博士, 讲师, 2007年、2010年分别于西安交通大学获学士、硕士学位, 2014年于法国特鲁瓦科技大学获得博士学位。主要从事数字图像处理、模式识别等领域的研究。E-mail:wangtian@buaa.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(61503017);航空科学基金资助项目(2016ZC51022)
- DOI：10.3788/OPE.20182603.0698
  中图分类号： TV211;TP751.1
- 收稿日期：2017-06-23，
  
  录用日期：2017-8-5，
  
  纸质出版日期：2018-03-25
- 稿件说明：
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王宇, 王田, 杨艺. 面向遥感图像水域分割的图像熵主动轮廓模型[J]. 光学精密工程, 2018,26(3):698-707.

Yu WANG, Tian WANG, Yi YANG. Image entropy active contour models towards water area segmentation in remote sensing image[J]. Optics and precision engineering, 2018, 26(3): 698-707.
王宇, 王田, 杨艺. 面向遥感图像水域分割的图像熵主动轮廓模型[J]. 光学精密工程, 2018,26(3):698-707. DOI： 10.3788/OPE.20182603.0698.

Yu WANG, Tian WANG, Yi YANG. Image entropy active contour models towards water area segmentation in remote sensing image[J]. Optics and precision engineering, 2018, 26(3): 698-707. DOI： 10.3788/OPE.20182603.0698.

摘要

为提高遥感图像水域分割的准确度，结合高分率遥感图像中水域与背景纹理复杂度差异较大的特点，将图像熵引入到CV模型中，提出两种图像熵主动轮廓模型用于高分辨率遥感图像的水域分割。其中，针对水域纹理相对简单的遥感图像，在CV模型中引入零水平集内的图像熵而构成局部图像熵主动轮廓模型，可以有效降低背景中灰度值与水域近似的区域发生误分，从而提高水域分割的准确度；针对水域纹理相对复杂的遥感图像，在CV模型中同时引入零水平集内外图像熵而构成全局图像熵主动轮廓模型，改进了水平集函数进化过程中对灰度信息的依赖，并能使零水平集进化到全局最优，进一步提高了遥感图像中水域分割的准确度。针对高分辨率遥感图像中的湖泊、河流和海域分割对比实验结果表明：局部图像熵主动轮廓模型的分割精确率分别为90.1%、81.5%和93.6%，

值分别为0.94、0.885和0.96；全局图像熵主动轮廓模型的分割精确率分别为94.5%、85.3%、94.9%，

值分别为0.956、0.895、0.967。本文提出的两种图像熵主动轮廓模型均能有效减小背景误分，提高了遥感图像水域分割的准确度。

Abstract

In order to improve the accuracy of water area segmentation in high resolution remote sensing image

the image entropy was introduced into CV model because there was a quite difference of texture complexities between water area and background

and two active counter models based on image entropy were proposed in this paper. The image entropy of inside zero level set was adopted in CV model and forms a local image entropy active counter model (LIEACM). This model effectively reduced the incorrect segmentation of background where the gray value approximated to the water area with low texture complexity. For remote sensing image of water area with high texture complexity

the global image entropy active counter model (GIEACM) was proposed

in which

the image entropy of inside and outside of zero level set were employed in CV model simultaneously. GLEACM modifies the fact that the level set function evolution depends on gray value

and the zero level set cald evaluate to the global optimal value. The experiments on segmentation the lake

river and sea validate that the segmentation precisions of LIFACM are 90.1%

81.5% and 93.6%

respectively

the

-measures are 0.94

0.885 and 0.96

respectively; and for GLEACM

the segmentation precisions are 94.5%

85.3% and 94.9%

respectively

the F-measures are 0.956

0.895 and 0.967

respectively. The two image entropy active contour models proposed by this paper improve the water area segmentation accuracy in remote sensing image effectively.

关键词

Keywords

references

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