[1]乔婷,张怀清,陈永富,等. 基于NDVI分割与面向对象的东洞庭湖湿地植被信息提取技术[J].西北林学院学报,2013,28(04):170-175.[doi:10.3969/j.issn.1001-7461.2013.04.35]
　QIAO Ting,ZHANG Huai-qing,CHEN Yong-fu,et al. Extraction of Vegetation Information Based on NDVI Segmentation and Object-oriented Method[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2013,28(04):170-175.[doi:10.3969/j.issn.1001-7461.2013.04.35]
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 基于NDVI分割与面向对象的东洞庭湖湿地植被信息提取技术()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第28卷
期数:
2013年04期
页码:
170-175
栏目:
出版日期:
2013-07-30

文章信息/Info

Title:
 Extraction of Vegetation Information Based on NDVI Segmentation and Object-oriented Method
文章编号:
1001-7461(2013)04-0170-06
作者:
 乔婷; 张怀清; 陈永富; 凌成星
 （中国林业科学研究院 资源信息研究所，北京 100091）
Author(s):
 QIAO Ting; ZHANG Huai-qing; CHEN Yong-fu; LING Cheng-xing
 (Research Institute of Forest Resource Information Techniques,CAF,Beijing 100091,China)
关键词:
SPOT-5影像; 面向对象分类; NDVI分割; 信息提取
Keywords:
SPOT-5 image;  object-oriented methodology;  NDVI segmentation;  information extractio
分类号:
S771.8
DOI:
10.3969/j.issn.1001-7461.2013.04.35
文献标志码:
A
摘要:
 针对东洞庭湖湿地植被的分布现状，为提高湿地植被信息提取的精度和效率，应用高分辨率的SPOT-5影像数据，在完成图像预处理的基础上，将NDVI应用到多尺度分割中，结合基于隶属度函数和阈值的面向对象的分类方法对东洞庭湖湿地植被信息进行提取；与此同时，以相同分类方法对未辅以NDVI分割的图像进行植被提取，并与最大似然监督分类法提取的结果进行对比。结果显示，辅以NDVI分割的面向对象信息提取的总分类精度达到了87.69%，Kappa系数达到0.86；未辅以NDVI的总分类精度为82.69%,Kappa系数为0.80;最大似然监督分类总分类精度71.92%，Kappa系数0.67；其总分类精度分别提高了5.00%、15.77%,Kappa系数分别提高了0.06、0.19。可见，该方法可以有效提高湿地植被的提取精度，为湿地植被资源进一步的监测和保护奠定了基础。
Abstract:
 In order to increase the extraction accuracy and efficiency of vegetation information,according to the vegetation distribution in the East Dongting Lake wetland,SPOT-5 remote sensing images were used as the data sources.After the images were pre-processed,NDVI was applied to the multi-scale segmentation to optimize vegetation extraction precise.Based on analyzing spectral,geometric and some other characteristics,the extraction of the East Dongting Lake wetland vegetation information was conducted by using membership functions and threshold of object-oriented classification aided by NDVI segmentation.Meanwhile,the vegetation information was also extracted by using the same object-oriented classification method without the application of NDVI.The extraction results were compared by the maximum likelihood supervised classification method.Satisfactory results were achieved by NDVI segmentation aided method with an overall accuracy of 87.69% and a KAPPA coefficient of 0.86.In contrast,the overall accuracy and Kappa coefficient of object-oriented classification method without NDVI were 82.69% and 0.80.The overall accuracy and Kappa coefficient of the maximum likelihood supervised classification were 71.92% and 0.67.The comparison indicated that object-oriented classification method could improve the classification accuracy.The application of NDVI in the segmentation could further improve the extraction accuracy of wetland vegetation.The method in this paper can achieve a more precise information extraction of the wetland vegetation.

备注/Memo

备注/Memo:
 收稿日期：2012-11-19 修回日期：2012-12-31
基金项目：林业科学技术推广项目（［2012］16号）；国家863计划课题(2009AA122003-L)；国家重大专项（E0305/1112/02）。
作者简介：乔婷，女，硕士研究生，研究方向：湿地资源监测。E-mail:fox-q@163.com
*通信作者：张怀清，男，研究员，硕士生导师，研究方向：三维可视化、湿地监测。E-mail:zhang@caf.ac.cn
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