[1]康乐,陈伟*,赵安琳,等. 基于IHS变换和Mean Shift算法的草地分类研究[J].西北林学院学报,2022,37(5):188-193.[doi:10.3969/j.issn.1001-7461.2022.05.27]
　KANG Le,CHEN Wei*,ZHAO An-lin,et al. Grassland Classification Based on IHS Transformation and Mean Shift Algorithm[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2022,37(5):188-193.[doi:10.3969/j.issn.1001-7461.2022.05.27]
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 基于IHS变换和Mean Shift算法的草地分类研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第37卷
期数:
2022年第5期
页码:
188-193
栏目:
出版日期:
2022-09-30

文章信息/Info

Title:
 Grassland Classification Based on IHS Transformation and Mean Shift Algorithm
文章编号:
1001-7461(2022)05-0188-06
作者:
 康乐1; 陈伟1*; 赵安琳1; 杨延征2
 （1.国家林业和草原局 华东调查规划设计院，浙江 杭州 310019；2.中国科学院 生态环境研究中心 城市与区域生态国家重点实验室，北京 100085）
Author(s):
 KANG Le1; CHEN Wei1*; ZHAO An-lin1; YANG Yan-zheng2
 (1.East China Inventory and Planning Institute,National Administration of Forestry and Grassland,Hangzhou 310019,Zhejiang,China; 2.State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-environmental Sciences,Chinese Academy of Sciences,100085,Beijing,China)
关键词:
 ; 草地分类; IHS; Mean Shift; 特征向量
Keywords:
 ; grassland classification;  IHS;  mean shift;  feature vector
分类号:
S716
DOI:
10.3969/j.issn.1001-7461.2022.05.27
文献标志码:
A
摘要:
 为高效估算草地生物量，需要一种方法来提高草地分类精度和降低数据处理时间。该研究基于原始RGB图像采用IHS变换，进行绿度波段图像的融合，并对融合后的图像进行Mean Shift算法分类。结果表明，1）基于IHS图像的草地分类，在视觉上与实际地物更为吻合；2）与其他文献方法对比，本研究方法性能优越，精度达到95%以上；3）可以批量处理多张图像，提高了数据处理效率。
Abstract:
 In order to estimate grassland biomass efficiently,a method is needed to improve grassland classification accuracy and reduce data processing time.Firstly,IHS transform was adopted based on the original RGB image,then the green band image was fused,and finally the fused image was classified by mean shift algorithm.Compared with many classification methods,the results showed that 1) the grassland classification based on IHS image was more consistent with the actual features visually.2) Compared with other methods in literature,this method had superior performance with the accuracy of more than 95%.3) It could process multiple images in batch,which greatly improved the data processing efficiency.

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备注/Memo

备注/Memo:
 收稿日期：2021-08-15修回日期：2021-12-07
基金项目：国家自然科学基金（41801181）。
第一作者：康乐，硕士，工程师。研究方向：森林经营管理、林草资源监测及自然保护地理论。E-mail:hdybhc@163.com
*通信作者：陈伟，博士，高级工程师。研究方向：林业信息化。E-mail:lajiao.1225@163.com
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