Image entropy active contour models towards water area segmentation in remote sensing image

Yu WANG; Tian WANG; Yi YANG

doi:10.3788/OPE.20182603.0698

您当前的位置：

首页 >

文章列表页 >

Image entropy active contour models towards water area segmentation in remote sensing image

Information Sciences | 更新时间：2020-07-05

- Image entropy active contour models towards water area segmentation in remote sensing image
- Optics and Precision Engineering Vol. 26, Issue 3, Pages: 698-707(2018)
- 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：
  
  YANG Yi, E-mail:yangyi@hpu.edu.cn
- 基金信息：
- DOI：10.3788/OPE.20182603.0698
  CLC： TV211;TP751.1
- Received：23 June 2017，
  
  Accepted：05 August 2017，
  
  Published：25 March 2018
- 稿件说明：
移动端阅览
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：

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

张建廷, 张立民.结合光谱和纹理的高分辨率遥感图像分水岭分割[J].武汉大学学报·信息科学版, 2017, 42(4):449-455, 467.

ZHANG J T, ZHANG L M. A watershed algorithm combining spectral and texture information for high resolution remote sensing image segmentation[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4):449-455, 467. (in Chinese)

GAETANO R, MASI G, POGGI G, et al .. Marker-controlled watershed-based segmentation of multiresolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(6):2987-3004.

SONG H H, HUANG B, ZHANG K H. A globally statistical active contour model for segmentation of oil slick in SAR imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(6):2402-2409.

王玉, 李玉, 赵泉华.可变类多光谱遥感图像分割[J].遥感学报, 2016, 20(6):1381-1390.

WANG Y, LI Y, ZHAO Q H. Integration of multispectral remote-sensing image segmentation with unknown number of classes[J]. Journal of Remote Sensing, 2016, 20(6):1381-1390. (in Chinese)

李玉, 徐艳, 赵雪梅, 等.利用高斯混合模型的多光谱图像模糊聚类分割[J].光学精密工程, 2017, 25(2):509-518.

LI Y, XU Y, ZHAO X M, et al .. Multispectral image segmentation by fuzzy clustering algorithm used Gaussian mixture model[J]. Opt. Precision Eng., 2017, 25(2):509-518. (in Chinese)

温奇, 王薇, 李苓苓, 等.高分辨率遥感影像的平原建成区提取[J].光学精密工程, 2016, 24(10):2557-2564.

WEN Q, WANG W, LI L L, et al .. Extraction of built-up area in plain from high resolution remote sensing images[J]. Opt. Precision Eng., 2016, 24(10):2557-2564. (in Chinese)

CHAN T, VESE L. An active contour model without edges[C]. Scale-Space Theories in Computer Vision, Springer, 1999, 1682: 141-151.

KASS M, WITKIN A, TERZOPOULOS D. Snakes:active contour models[J]. International Journal of Computer Vision, 1988, 1(4):321-331.

CASELLES V, KIMMEL R, SAPIRO G. Geodesic active contours[J]. International Journal of Computer Vision, 1997, 22(1):61-79.

NIU S J, CHEN Q, DE SISTERNES L, et al .. Robust noise region-based active contour model via local similarity factor for image segmentation[J]. Pattern Recognition, 2017, 61:104-119.

LI CH M, GORE J C, DAVATZIKOS C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation[J]. Magnetic Resonance Imaging, 2014, 32(7):913-923.

刘建磊, 隋青美, 朱文兴.结合概率密度函数和主动轮廓模型的磁共振图像分割[J].光学精密工程, 2014, 22(12):3435-3442.

LIU J L, SUI Q M, ZHU W X. MR image segmentation based on probability density function and active contour model[J]. Opt. Precision Eng., 2014, 22(12):3435-3442. (in Chinese)

ZHANG K H, ZHANG L, SONG H H, et al .. Active contours with selective local or global segmentation:A new formulation and level set method[J]. Image and Vision Computing, 2010, 28(4):668-676.

HOOGI A, SUBRAMANIAM A, VEERAPANENI R, et al . Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis[J]. IEEE Transactions on Medical Imaging, 2017, 36(3):781-791.

苏丽, 吴俊杰, 庞迪.基于改进主动轮廓模型的全景海天线检测[J].光学学报, 2016, 36(11):1115003.

SU L, WU J J, PANG D. Panoramic sea-sky-line detection based on improved active contour model[J]. Acta Optica Sinica, 2016, 36(11):1115003. (in Chinese)

姜大伟, 范剑超, 黄凤荣. SAR图像海岸线检测的区域距离正则化几何主动轮廓模型[J].测绘学报, 2016, 45(9):1096-1103.

JIANG D W, FAN J CH, HUANG F R. SAR image coastline detection based on regional distance regularized geometric active contour models[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9):1096-1103. (in Chinese)

HAN B, WU Y Q. A novel active contour model based on modified symmetric cross entropy for remote sensing river image segmentation[J]. Pattern Recognition, 2017, 67:396-409.

TIAN B S, LI ZH, ZHANG M M, et al .. Mapping thermokarst lakes on the Qinghai-Tibet Plateau using nonlocal active contours in Chinese GaoFen-2 multispectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(5):1687-1700.

LI C M, XU CH Y, GUI CH F, et al .. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12):3243-3254.

ALIREZA. K-means, mean-shift and normalized-cut segmentation[EB/OL]. (2015-08-27). http://cn.mathworks.com/matlabcentral/fileexchange/52698-k-means-mean-shift-and-normalized-cut-segmentation http://cn.mathworks.com/matlabcentral/fileexchange/52698-k-means-mean-shift-and-normalized-cut-segmentation .

Views

486

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Spatial adaptation and frequency fusion network for single remote sensing image super-resolution

Generate adversarial network for super-resolution reconstruction of remote sensing images by fusing edge enhancement and non-local modules

Fast extraction of buildings from remote sensing images by fusion of CNN and Transformer

Fusion of fractal geometric features Resnet remote sensing image building segmentation

Building segmentation in remote sensing image based on multiscale-feature fusion dilated convolution resnet

Related Author

YANG Yichuan

MA Zhongqi

ZHOU Xinyao

ZHENG Fujian

HUANG Hong

LIU Jie

QI Ruo

HAN Ke

Related Institution

Beijing Institute of Space Machinery and Electronics

Key Laboratory of Optoelectronic Technology and System， Ministry of Education， Chongqing University

College of Measurement and Control Technology and Communication Engineering， Harbin University of Science and Technology

School of Computer and Information Engineering， Harbin University of Commerce

Hebei Key Laboratory of Electromagnetic Environmental Effects and Information Processing， Shijiazhuang Tiedao University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰