[1]刘晓双,龚直文,吴 见.基于多特征的高光谱遥感土地利用信息提取[J].南京林业大学学报(自然科学版),2018,42(04):141-147.[doi:10.3969/j.issn.1000-2006.201705029]
 LIU Xiaoshuang,GONG Zhiwen,WU Jian.Land use information extraction using multiple features derivedfrom hyperspectral images[J].Journal of Nanjing Forestry University(Natural Science Edition),2018,42(04):141-147.[doi:10.3969/j.issn.1000-2006.201705029]
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基于多特征的高光谱遥感土地利用信息提取
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《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

卷:
42
期数:
2018年04期
页码:
141-147
栏目:
研究论文
出版日期:
2018-07-12

文章信息/Info

Title:
Land use information extraction using multiple features derived from hyperspectral images
文章编号:
1000-2006(2018)04-0141-07
作者:
刘晓双1龚直文2吴 见3
1.国家林业局西北林业调查规划设计院,陕西 西安 710048; 2.西北农林科技大学经济管理学院, 陕西 杨凌 712100; 3.滁州学院地理信息与旅游学院,安徽 滁州 239000
Author(s):
LIU Xiaoshuang1 GONG Zhiwen2 WU Jian3
1. Northwest Institute of Forest Inventory, Planning and Design, SFA, Xi'an 710048, China; 2. College of Economics and Management,Northwest A & F University, Yangling 712100, China; 3. Geography Information and Tourism College, Chuzhou University, Chuzhou 239000, China
关键词:
高光谱 纹理特征 空间信息 土地利用 遥感数据 分类精度
Keywords:
Keywords:hyperspectral textural features spatial information land use remote sensing classification accuracy
分类号:
TP79
DOI:
10.3969/j.issn.1000-2006.201705029
文献标志码:
A
摘要:
【目的】为了降低高光谱遥感数据噪声,提高土地利用分类信息提取精度,探索结合纹理和空间信息的分类方法。【方法】以河南镇平县Hyperion高光谱成像光谱仪获取的高光谱影像为数据源,借鉴决策树分类思想,采用了一种结合光谱、纹理和空间信息的高光谱遥感多特征地类提取方法,先通过提取光谱特征初步提取地类,再分别采用提取纹理特征和基于空间信息的植被提取进行详细地类信息的分层提取,最后,用地面实测样点验证各类土地利用类型的分类精度,比较了用不同方法对不同地类的提取效果。【结果】基于多特征的地类分层提取体系中,采用各波段光谱反射率区分大的地类,再用纹理特征进行光谱差异较小的地类划分,而基于空间信息进行植被的分类。通过结合纹理和空间信息提取方法的总分类精度达86.7%,较最大似然法分类精度提高13.3%。【结论】高光谱与纹理和空间信息相结合的遥感分类方法能有效减小噪声,提高分类精度,可为土地利用分类提取研究提供一定的参考。
Abstract:
Abstract: 【Objective】To reduce noise and improve land use classification accuracy, we need to explore a classification method that combines texture and spatial information. 【Method】Using remote sensing images obtained from hyperspectral imaging spectrometers in Henan Zhenping and learning from the classification of a decision tree, a hyperspectral remote sensing classification method combining spectrum, texture, and spatial information was used. The land use type was initially determined by extracting the spectral features, and then hierarchically determined in detail by extracting the texture features and vegetation, which was based on spatial information. Finally, the accuracy of the classification of various land use types was verified by ground test points, and the extraction efficiency of different methods was compared. 【Result】The land-type layered-extraction system combined many characteristics, adopted various band spectral reflectances to classify large ground classes, used textural features to classify ground classes with smaller spectral differences, and then classified vegetation based on spatial information. The total classification accuracy of the method combining texture and spatial information was 86.7%, which was 13.3% higher than the total accuracy of maximum likelihood classification. 【Conclusion】The remote sensing classification method that combined hyperspectral and texture features, and spatial information can effectively reduce noise and improve the classification accuracy. This provides a reference for the study of land use classification.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金青年科学基金项目(31400540); 陕西省自然科学基金项目(2013JQ5013); 安徽省自然科学青年基金项目(1808085QC72); 安徽高校自然科学研究重点项目(KJ2018A0434) 第一作者:刘晓双(xinz_77@sina.com),工程师。
更新日期/Last Update: 2018-07-27