| 黄巧义，张 木，黄 旭，李 苹，付弘婷，张发宝，唐拴虎.基于可见光谱色彩指标Otsu 法的水稻冠层图像分割[J].广东农业科学,2018,45(1):120-125 |
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基于可见光谱色彩指标Otsu 法的水稻冠层图像分割 |
| Segmentation of rice canopy image using the Otsu method based on visual spectral image color related indices |
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| DOI：10.16768/j.issn.1004-874X.2018.01.020 |
| 中文关键词: 最大类间方差法（Otsu 法） 水稻 数字图像 图像分割 |
| 英文关键词: Otsu’s method Rice Digital image Segmentation |
| 基金项目:国家公益性行业（农业）科研专项（201503123）；广东农业面源污染控制创新团队：省属科研机构改
革创新领域项目（2016B070701009）；广东省科技计划项目（2016A020210035，2014B090904068） |
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| 摘要点击次数: 2051 |
| 全文下载次数: 809 |
| 中文摘要: |
| 水稻冠层数字图像分析技术在水稻生长监测、氮营养诊断及产量预测上具有应用潜力，而水稻
像元精确分割、提取是水稻冠层数字图像分析结果准确、稳定、可靠的前提。最大类间方差法（Otsu 法）具
备分割质量稳定、自适应强的特性，分割效果较好，是一种常用的阈值分割方法。通过提取计算水稻冠层图
像9 种图像色彩指标R、G、B、CIEL*a*b* 色彩空间的L*、a*、b* 分量、HSV 色彩空间的H 分量、绿度叶片
指数（GLD）以及植被指数（VIGreen），并以各种图像色彩指标的Otsu 法对水稻冠层图像进行分割，比较其
图像分割效果。结果表明，水稻和土壤像元的a*、b*、GLD、VIGreen 色彩指标双峰性明显，且重叠性小，可作
为分割水稻与土壤背景的候选图像色彩指标；基于a*、GLD、VIGreen 色彩指标的Otsu 法的分割精度较高，且
基于a* 色彩指标的Otsu 法对水稻冠层图像分割效果的信噪比最大、误差率最低，其次是基于VIGreen 色彩
指标的Otsu 法；基于CIEL*a*b* 色彩空间的a* 色彩指标是Otsu 法的水稻冠层图像分割中较优的图像色彩
指标。 |
| 英文摘要: |
| Digital image analysis of rice canopy has been widely for monitoring rice growth，diagnosis rice N
status，and predicting rice yield. The accuracy，stability，and reliability of the rice canopy digital image analysis
result were based on the premise of precise segmentation of rice pixels. Otsu’s method was the frequently used
thresholding method in image segmentation，for its stability and self-adaptation. This paper extracted and calculated
9 image color indices from digital camera images using three color models： R，G，B，L*，a*，b* components of
CIEL*a*b* color space，H，visible-spectrum green leaf algorithm（ GLD） and vegetation index（VIGreen）. The
segmentation of rice pixel from soil background was conducted by the Otsu’s method based on different color indices，and the accuracy of the segmentation was evaluated. The results showed that there were obvious bimodality
and little overlap in a*，b* components of CIEL*a*b* color space，GLD，and VIGreen between rice and soil pixels，
which mean that these color indices should be the candidate color indices for rice canopy image segmentation. The
Otsu’s method based on a* components of CIEL*a*b* color space，GLD，and VIGreen had higher segmentation
accuracy in comparison with which based on b* components of CIEL*a*b* color space. The highest signal-noise ratio
and lowest error rate of rice canopy image segmentation was arrived by the Otsu’s method based on a* components
of CIEL*a*b* color space. The signal-noise ratio and error rate of rice canopy image segmentation by the Otsu’s
method based on VIGreen were next only to the Otsu’s method based on a* components of CIEL*a*b* color space.
In conclusion，a* components of CIEL*a*b* color space was the optimum image color index for rice canopy image
segmentation by the Otsu’s method. |
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