[1]陈科尹,吴崇友,关卓怀,等.基于统计直方图k-means聚类的水稻冠层图像分割[J].江苏农业学报,2021,(06):1425-1435.[doi:doi:10.3969/j.issn.1000-4440.2021.05.009]
　CHEN Ke-yin,WU Chong-you,GUAN Zhuo-huai,et al.Rice canopy image segmentation based on statistical histogram k-means clustering[J].,2021,(06):1425-1435.[doi:doi:10.3969/j.issn.1000-4440.2021.05.009]
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基于统计直方图k-means聚类的水稻冠层图像分割()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年06期
页码:
1425-1435
栏目:
耕作栽培·资源环境
出版日期:
2021-12-30

文章信息/Info

Title:
Rice canopy image segmentation based on statistical histogram k-means clustering
作者:
陈科尹1; 2; 吴崇友2; 关卓怀2; 李海同2; 王刚2
(1.嘉应学院物理与电子工程学院，广东梅州514015；2.农业农村部南京农业机械化研究所，江苏南京210014)
Author(s):
CHEN Ke-yin1; 2; WU Chong-you2; GUAN Zhuo-huai2; LI Hai-tong2; WANG Gang2
(1.School of Physics and Electronic Engineering, Jiaying University, Meizhou 514015, China；2.Nanjing Institute of Agricultural Mechanization，Ministry of Agriculture and Rural Affairs， Nanjing 210014, China)
关键词:
水稻冠层图像; 图像分割; 统计直方图; k-means聚类
Keywords:
rice canopy image; image segmentation; statistical histogram; k-means clustering
分类号:
S126
DOI:
doi:10.3969/j.issn.1000-4440.2021.05.009
文献标志码:
A
摘要:
针对现有k-means聚类图像分割方法存在对初始聚类中心敏感、易错分割以及运行时效低等问题，提出了一种基于统计直方图k-means聚类的水稻冠层图像分割方法。该方法首先根据图像直方图蕴含的像素数量先验信息，选择像素数量差异较大的像素值作为水稻冠层图像的初始聚类中心；然后再利用图像直方图中像素值与图像像素数量的先验对应关系，对水稻冠层图像聚类目标函数权值化；最后依据k-means聚类框架对水稻冠层图像进行聚类分割。为了验证本方法的有效性，分别同基于k-means、k-means++、k-mc2、afk-mc2等4种主流均值聚类的水稻冠层图像特征像素提取方法进行对比试验。结果表明：对于临稻20号、武运粳32号以及中粳798号成熟中期水稻冠层图像聚类分割，常光下本方法的平均类内平方差分别为36.6、29.5、29.5，平均类间平方差分别为67.5、51.8、51.8，平均运行时间分别为0.2 s、0.3 s、0.3 s；强光下本方法的平均类内平方差分别为15.2、4.9、12.1，平均类间平方差分别为30.9、9.5、21.2，平均运行时间分别为0.3 s、0.2 s、0.3 s，均优于以上4种聚类方法。
Abstract:
A rice canopy image segmentation method based on statistical histogram k-means clustering was proposed to solve the problems of existing k-means methods, such as sensitivity to the initial cluster center, error prone segmentation and low operation efficiency. Firstly, according to the image histogram, the pixel value with large difference in the number of pixels was selected as the initial cluster center of the rice canopy image. Secondly, the prior correspondence between the pixel value in the image histogram and the number of pixels in the image was used to weight the clustering objective function. Finally, the k-means clustering framework was used to cluster and segment the rice canopy image. In order to verify the effectiveness of the proposed method, comparative experiments were carried out with the feature pixel extraction methods of rice canopy images based on k-means, k-means++, k-mc2 and afk-mc2. The results showed that the canopy images of Lindao 20, Wuyunjing 32 and Zhongjing 798 were clustered and seymented at mid-maturing stage, and the average intra-class sequare errors were 36.6， 29.5 and 29.5 under normal light conditions, the average inter-class square errors were 67.5, 51.8 and 51.8, and the average running time was 0.2 s, 0.3 s and 0.3 s. Under strong light conditions, the average intra-class square errors were 15.2, 4.9 and 12.1, the average inter-class square errors were 30.9, 9.5 and 21.2, and the average running time was 0.3 s, 0.2 s and 0.3 s. The results of the method used in this study are better than the above four traditional clustering methods.

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备注/Memo

备注/Memo:
收稿日期：2021-05-07基金项目：国家重点研发计划项目 ( 2016YFD0702100 )；国家自然科学地区基金项目( 61863011)；广东省普通高校特色创新项目(2020KTSCX142)；中国农业科学院基本科研业务费专项(SR201919)作者简介：陈科尹( 1982-) ，男，广东雷州人，博士，讲师，从事农业图像处理、机器视觉方面研究。(E-mail) chenkeyin10@126.com通讯作者：吴崇友，(E-mail)542681935@ qq com
更新日期/Last Update: 2022-01-07