[1]张小虎,黄芬,陈佳悦,等.基于图像增强和α角度模型的K均值小麦冠层分割算法的改进[J].南京农业大学学报,2018,41(3):413-421.[doi:10.7685/jnau.201705015]
 ZHANG Xiaohu,HUANG Fen,CHEN Jiayue,et al.K-means clustering segmentation for wheat canopy image based on image enhancement and alpha angle model[J].Journal of Nanjing Agricultural University,2018,41(3):413-421.[doi:10.7685/jnau.201705015]
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基于图像增强和α角度模型的K均值小麦冠层分割算法的改进()
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《南京农业大学学报》[ISSN:1000-2030/CN:32-1148/S]

卷:
41卷
期数:
2018年3期
页码:
413-421
栏目:
出版日期:
2018-05-15

文章信息/Info

Title:
K-means clustering segmentation for wheat canopy image based on image enhancement and alpha angle model
作者:
张小虎1 黄芬12 陈佳悦23 高翔2 刘铭2 姚霞1 朱艳1
1. 南京农业大学国家信息农业工程技术中心, 江苏 南京 210095;
2. 南京农业大学信息科学与技术学院, 江苏 南京 210095;
3. 中国移动通信集团浙江有限公司嘉兴分公司, 浙江 嘉兴 314000
Author(s):
ZHANG Xiaohu1 HUANG Fen12 CHEN Jiayue23 GAO Xiang2 LIU Ming2 YAO Xia1 ZHU Yan1
1. National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China;
2. College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;
3. Jiaxing Branch of China Mobile Communication Group Zhejiang Co., Ltd., Jiaxing 314000, China
关键词:
小麦冠层图像分割图像增强脉冲耦合神经网络同态滤波α角度模型L*a*b*颜色空间
Keywords:
wheat canopy image segmentationimage enhancementpulse coupled neural network(PCNN)homomorphic filteringalpha angle modelL*a*b* color space
分类号:
S126;TP391.4
DOI:
10.7685/jnau.201705015
摘要:
[目的]本文旨在克服光照不均引起的低对比度、反光、阴影、光斑及遮挡等对大田复杂背景下小麦冠层图像分割的干扰。[方法]设计了一种结合脉冲耦合神经网络(pulse coupled neural network,PCNN)与同态滤波的自适应图像增强和基于L*a*b*颜色空间α角度模型的K均值聚类分割算法。首先,将小麦冠层图像转换到HSI颜色空间,采用自适应算法对HSI空间的I分量进行增强处理,适当调节饱和度S分量,补偿光照强度分布不均,去除阴影及拉大对比度;其次,将增强处理后的图像映射到L*a*b*颜色空间,提取a*b*分量建立α角度模型;最后,基于α进行K均值聚类分割处理。[结果]拔节前后光照强度不一、光照不均的冬小麦冠层图像的分割试验结果表明,该算法可一定程度避免基于L*a*b*颜色空间α角度分量K均值聚类的过分割现象;改善基于HSI空间H分量K均值聚类的欠分割缺陷,且对光斑、阴影遮挡、反光突出的图像分割更完整准确。[结论]本算法可为大田复杂背景下光照多变的作物冠层图像分割提供参考方法。
Abstract:
[Objectives] The paper aims to improve the segmentation accuracy of wheat canopy image under the low contrast, reflections, shadows, light and shade caused by uneven illumination, complex background in the field. [Methods] This paper designed a K-means clustering segmentation algorithm based on alpha angle model of L*a*b* color space after adaptive image was enhanced and combined with pulse coupled neural network(PCNN)and homomorphic filtering. Firstly, convert the wheat canopy image from RGB to HSI color space, and process the I component of HSI space by the adaptive enhancement algorithm, and properly adjust the saturation S component, so that it could compensate for the uneven distribution of the light intensity, remove the shadow and widen the contrast ratio. Secondly, map processed and enhanced image to the L*a*b* color space, extract a*, b* component, and establish alpha angle model which had smaller related degree with luminance component L*, was more suitable for being feature vector of K-means clustering. Finally, process the enhanced wheat used K-means clustering segmentation. [Results] Segmentation experiment results of winter wheat canopy images which had different light intensity, uneven illumination with before jointing stage and later jointing stage showed that:in allusion to wheat images of different illumination in different growth periods, the segmentation algorithm in this paper was better. To some degree, it could avoid over-segmentation phenomenon compared with the segmentation method of K-means clustering segmentation algorithm based on alpha angle model of L*a*b* color space without being enhanced;Compared with algorithm based on the normalized K-means clustering segmentation for the H weight, for the image of stronger reflectivity, it could also keep more complete details of wheat leaf and had better noise immunity of shadow, and for the image of the spot by the sun and the shadow of shade, it could segment more completely and accurately. [Conclusions] In conclusion, the segmentation algorithm in this paper could provide a reference method for crop canopy image segmentation which had changeable illumination under complex background field.

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备注/Memo

备注/Memo:
收稿日期:2017-05-09。
基金项目:国家重点研发计划项目(2016YFD0300607);江苏省农业科技自主创新资金项目[CX(14)2116]
作者简介:张小虎,博士,讲师,研究方向为农业信息,E-mail:365688495@qq.com。
通信作者:黄芬,博士,副教授,研究方向为数据挖掘、图像处理,E-mail:fenhuang@njau.edu.cn。
更新日期/Last Update: 1900-01-01