计算机工程与应用2018，Vol.54Issue(2)：177-181,5.DOI:10.3778/j.issn.1002-8331.1608-0084
基于SLIC方法的光照偏强农田图像分割研究
摘要
Abstract
Precision agriculture is the trend of agricultural development, while farmland image segmentation is the pre-mise and foundation of precision agriculture. Since the effect of farmland images highlights areas lost plant features on the quality of the images segmentation,based on the SLIC method and Cg component in YCrCb color space,taking use of different classifiers,this paper realizes farmland images segmentation under the condition of strong light.Firstly,farmland images are pre-processing using SLIC,to obtain super pixel region.To avoid plant pages losing green features because of the highlight areas under strong light, this paper introduces the Cg component in YCrCb color space and excess green characteristic.To avoid high requirements for the training sample in supervised learning,this paper uses semi-supervised learning,mixes up the labeled samples with samples without a label.Finally,images segmentation takes different classifi-ers,and the quality of image segmentation is evaluated by confusion matrix and Kappa coefficient.Contrasting the experi-mental results,the kernel function which is diagQuadratic in distance discriminance is better than other methods,the accu-racy is higher.关键词
图像分割/不同分类器/简单的线性迭代聚类(SLIC)方法/Cg分量/光照偏强Key words
image segmentation/different classifiers/Simple Linear Iterative Clusterign(SLIC)method/Cg component/strong light分类
信息技术与安全科学引用本文复制引用
陈晓倩,唐晶磊,王栋..基于SLIC方法的光照偏强农田图像分割研究[J].计算机工程与应用,2018,54(2):177-181,5.基金项目
国家自然科学基金(No.31101075) （No.31101075）
国家高技术研究发展计划(863)(No.2013AA10230402) （863）
西安市科技计划项目(No. NC1504(2)). （No. NC1504(2）
