[1]陈 工,李 琦,张彦南,等.多源遥感信息提取桉树人工林[J].浙江林业科技,2018,38(02):78-87.[doi:10.3969/j.issn.1001-3776.2018.02.013]
 CHEN Gong,LI Qi,ZHANG Yan-nan,et al.Classification of Eucalyptus Plantation Based on Multiple Remote Sensing Data[J].Journal of Zhejiang Forestry Science and Technology,2018,38(02):78-87.[doi:10.3969/j.issn.1001-3776.2018.02.013]
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多源遥感信息提取桉树人工林()
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《浙江林业科技》[ISSN:1001-3776/CN:33-1112/S]

卷:
38
期数:
2018年02期
页码:
78-87
栏目:
应用技术
出版日期:
2018-05-31

文章信息/Info

Title:
Classification of Eucalyptus Plantation Based on Multiple Remote Sensing Data
作者:
陈 工12李 琦1张彦南1梁鹤鸣1陈劲松1刘 强3
1. 北京大学 地球与空间科学学院,北京 100871;2. 中国科学院 深圳先进技术研究院,广东 深圳 518055; 3. 国家电网门供电公司,湖北 荆门 448000
Author(s):
CHEN Gong12LI Qi1ZHANG Yan-nan1LIANG He-ming1CHEN Jing-song1LIU Qiang3
1. School of Earth and Space Science, Beijing University, Beijing 100871, China; 2. Shenzhen Institutes of Advanced Technology, CAS, Shenzhen 518055, China; 3.Jingmen Branch, State Grid Corporation of China, Jingmen 448000, China
关键词:
桉树人工林决策树面向对象分类分割纹理特征
Keywords:
eucalyptus plantations decision tree object-oriented classification segmentation texture feature
分类号:
S792.39
DOI:
10.3969/j.issn.1001-3776.2018.02.013
文献标志码:
A
摘要:
针对桉树人工林在遥感影像上与自然林光谱差异小难以区分的问题,研究选取Quickbird,Landsat 8,数字 高程模型(DEM)等多源遥感数据,利用决策树加面向对象的分类方法提取桉树林。在多时相Landsat 8 提取兴 趣区域(ROI)基础上,利用最优尺度(ESP)工具计算研究区最优分割尺度进行分割,然后在高分辨率遥感影像 上进行灰度共生矩阵分析,选取最佳纹理参量,最后结合光谱、DEM、SLOPE 信息,选择相应参数构建分类决策 树,利用面向对象方法提取桉树林。最后对桉树提取结果进行精度评价,使用该方法与用Quickbird、利用TM 提 取精度分别为89.7%,83.1%,69.8%。表明本方法能够综合多源遥感信息,可以快速、较高精度地提取桉树人工 林,具有一定的应用价值。
Abstract:
High resolution Quickbird images in April 2014, that of Landsat 8 during the year of 2014 and digital elevation model in 2013 in Heyuan, Guangdong province were selected as the data sources for classification of eucalyptus plantation, with decision tree and object-oriented classification method. Region of interest was selected in multidate Landsat 8, the optimal segmentation scale was calculated by estimate of scale parameter, and analysis on the high resolution remote sensing images by gray level co-occurrence matrix, best texture parameters were selected. Classification decision tree was selected by parameters from spectrum, DEM and SLOPE information, and eucalyptus plantation was classified by object-oriented method. The classification accuracy reached 89.7%.

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备注/Memo

备注/Memo:
收稿日期:2017-07-30;修回日期:2018-01-27 基金项目:国家科技支撑计划项目(2012BAC20B06) 作者简介:陈工,博士生,从事遥感、GIS 和人工智能研究;E-mail:gong.chen@pku.edu.cn。通信作者:李琦,教授,博士生导师,从事 智慧城市研究;E-mail:qi.lee009@gmail.com。
更新日期/Last Update: 2018-06-15