[1]郭宝华,范少辉*,官凤英*,等. 基于支持向量机的竹林信息提取研究[J].西北林学院学报,2014,29(02):80-84.[doi:doi:10.3969/j.issn.1001-7461.2014.02.14]
 GUO Bao-hua,FAN Shao-hui*,GUAN Feng-ying*,et al. Bamboo Information Extraction Based on Support Vector Machine[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2014,29(02):80-84.[doi:doi:10.3969/j.issn.1001-7461.2014.02.14]
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 基于支持向量机的竹林信息提取研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第29卷
期数:
2014年02期
页码:
80-84
栏目:
出版日期:
2014-03-30

文章信息/Info

Title:
 Bamboo Information Extraction Based on Support Vector Machine
文章编号:
1001-7461(2014)-02-0080-05
作者:
 郭宝华1范少辉1*官凤英1*黄永南2
 (1.国际竹藤中心 竹藤科学与技术重点实验室,北京 100102; 2.福建省永安市林业局,福建 永安 366000 )
Author(s):
 GUO Bao-hua1 FAN Shao-hui1*GUAN Feng-ying1* HUANG Yong-Nan2
 (1. International Centre for Bamboo and Rattan, Key Laboratory of Bamboo and Rattan, Beijing 100102, China; 2. Yong’an Forestry Buraeu, Yong’an ,Fujian 366000, China)
关键词:
 支持向量机Landsat TM影像竹林精度评价
Keywords:
 support vector machine (SVM) Landsat TM image bamboo precision evaluation
分类号:
S711
DOI:
doi:10.3969/j.issn.1001-7461.2014.02.14
文献标志码:
A
摘要:
 竹资源的消长、变化对区域经济可持续发展和生态平衡维护具有重要作用,信息提取是应用遥感技术对竹资源监测和管理的基础,以TM遥感影像为基础,采用支持向量机(SVM)方法对福建省顺昌县的竹资源信息进行提取,并与传统的最大似然分类法进行比较。结果表明:基于支持向量机方法提取精度达到81.01%,kappa 系数为0.77;该方法比最大似然法精度高,并且操作简单和适用性强。
Abstract:
 Growth and decline changes of bamboo resources play an important role in sustainable development of regional economy and ecological balance, and information extraction is the foundation of monitoring and management bamboo resources using remote sensing technology. Based on TM remote sensing image, the bamboo information of Shunchang County, Fujian province was extracted by using the support vector machine (SVM) method. The result was compared with the traditional maximum likelihood classification. The extraction accuracy of SVM method reached 81.01%, and the Kappa index was 0.77. The accuracy of SVM that was simple to operate and apply was higher than maximum likelihood method.

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备注/Memo

备注/Memo:
 收稿日期:2013-08-14 修回日期:2013-10-11
基金项目:国际竹藤中心科研专项(1632013010);“十二五”国家科技支撑计划项目(2012BAD23B04); 林业科技成果推广项目([2012]36号);江西省财政林业重大专项(2011511101); 国家林业局948项目(2013-4-70)。
作者简介:郭宝华,男, 助理研究员,博士研究生, 研究方向:森林培育。 E-mail:bhguo@ icbr.ac.cn
*通信作者:范少辉,男,研究员,研究方向:竹林培育与经营。E-mail:fansh@icbr.ac.cn
官凤英,女, 副研究员,研究方向:竹资源监测与管理技术。E-mail:guanfy@icbr.ac.cn
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