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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 96-101.doi: 10.6040/j.issn.1672-3961.0.2018.242

• 机器学习与数据挖掘 • 上一篇    下一篇

基于多方法融合的非监督彩色图像分割

董新宇1,2,3(),陈瀚阅1,2,*(),李家国3,孟庆岩3,邢世和1,2,张黎明1,2   

  1. 1. 福建农林大学 资源与环境学院，福建 福州 350002
    2. 土壤生态系统健康与调控福建省高等学校重点实验室，福建 福州 350002
    3. 中国科学院遥感与数字地球研究所，北京 100101
  • 收稿日期:2018-06-07 出版日期:2019-04-20 发布日期:2019-04-19
  • 通讯作者: 陈瀚阅 E-mail:dsammy@126.com;chenhanyue.420@163.com
  • 作者简介:董新宇(1994—)，男，河南商丘人，硕士研究生，主要研究方向为遥感图像处理.E-mail: dsammy@126.com
  • 基金资助:
    海南自然科学基金创新研究团队资助项目(2017CXTD015);高分辨率对地观测系统重大专项资助项目(30-Y20A07-9003-17/18);国家自然科学基金资助项目(41401399)

An unsupervised color image segmentation method based on fusion of multiple methods

Xinyu DONG1,2,3(),Hanyue CHEN1,2,*(),Jiaguo LI3,Qingyan MENG3,Shihe XING1,2,Liming ZHANG1,2   

  1. 1. College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China
    2. University Key Lab of Soil Ecosystem Health and Regulation in Fujian, Fuzhou 350002, Fujian, China
    3. The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, Beijing, China
  • Received:2018-06-07 Online:2019-04-20 Published:2019-04-19
  • Contact: Hanyue CHEN E-mail:dsammy@126.com;chenhanyue.420@163.com
  • Supported by:
    海南自然科学基金创新研究团队资助项目(2017CXTD015);高分辨率对地观测系统重大专项资助项目(30-Y20A07-9003-17/18);国家自然科学基金资助项目(41401399)

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摘要：

针对传统K-means聚类彩色图像分割方法需要人为设定初始分割类别数目、易受噪声干扰等缺陷，提出一种多方法融合非监督彩色图像分割算法。该算法对原始图像进行光谱信息增强处理以提高图像信息提取效率，对K-means聚类引入戴维森堡丁指数(Davies-Bouldin index, DBI)自动化确定最佳分割类别数目，通过图像聚类分析并进行像素标签标记，并结合高斯马尔科夫随机场(Gauss-Markov random field, GMRF)理论对标记图像进行分割，最后使用形态学算子进行后处理完成分割操作。试验结果表明。本研究方法具有一定的鲁棒性，且分割效果更接近真实性。通过对分割结果进行量化评价，进一步说明本研究方法在分割精度和准确性方面更具优势。

关键词: 彩色图像分割, 去相关拉伸, K-means聚类, 高斯马尔科夫随机场, 数学形态学算子

Abstract:

An unsupervised color image segmentation method based on fusion of multiple methods was proposed, which considered the defects of traditional K-means clustering color image segmentation method, such as the need to set the number of initial segmentation categories artificially and the vulnerability to noise interference, etc. First of all, the original image was processed by spectral information enhancement to improving the efficiency of image information extraction. Next, the number of K-means clustering segmentation categories was determined automatically by using Davies-Bouldin Index, and the clustering analysis was carried out for images and each pixel in an image was labeled. Then, the labeled image was segmented by combining the Gauss-Markov random field theory. Finally, the image after-processing was made based on the morphological operators. The segmentation experiments were carried out by using different methods, the results showed that the segmentation effect of the proposed method was closer to the origin image, and the proposed method had good robustness. And the results of quantitative evaluation of segmentation showed that this method had more advantages in segmentation precision and accuracy.

Key words: color image segmentation, decorrelation stretch, K-means clustering, Gauss-Markov random field, morphological operators

中图分类号: 

  • TP751

图1

原始影像"

图2

去相关拉伸处理结果"

表1

去相关拉伸处理前后波段的相关系数"

阶段 波段 Band1 Band2 Band3
Band1 1.000 0 0.976 1 0.772 0
处理前 Band2 0.976 1 1.000 0 0.884 5
Band3 0.772 0 0.884 5 1.000 0
Band1 1.000 0 0.308 7 0.002 9
处理后 Band2 0.308 7 1.000 0 0.039 3
Band3 0.002 9 0.039 3 1.000 0

图3

12003号图片分割结果对比"

图4

124084号图片分割结果对比"

图5

24036号图片分割结果对比"

表2

Dice系数对比"

编号 本研究算法 K-means FCM FRFCM
12003 0.968 8 0.880 3 0.796 6 0.787 6
124084 0.960 8 0.831 4 0.434 7 0.909 6
24036 0.982 6 0.978 6 0.319 3 0.979 6

表3

Jaccard相似性系数对比"

编号 本研究算法 K-means FCM FRFCM
12003 0.939 4 0.786 1 0.662 0 0.649 6
124084 0.934 1 0.711 5 0.277 7 0.834 1
24036 0.965 8 0.958 1 0.190 0 0.959 9
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