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遥感技术与应用  2019, Vol. 34 Issue (6): 1269-1275    DOI: 10.11873/j.issn.1004-0323.2019.6.1269
数据与图像处理     
基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究
刘培1,2(),余志远1,2,4,马威3,韩瑞梅1,2(),陈正超4,王涵1,2,杨磊库1,2
1.河南理工大学 矿山空间信息技术国家测绘与地理信息局重点实验室,河南 焦作 454003
2.河南理工大学 测绘与国土信息工程学院,河南 焦作 454003
3.江苏省地质测绘院 江苏 南京,210000
4.中国科学院遥感与数字地球研究所 北京 100094
Remotely Sensed Data Classification by Collaborative Processing of Landsat, Radarsat-2 and Topography Information
Pei Liu1,2(),Zhiyuan Yu1,2,4,Wei Ma3,Ruimei Han1,2(),Zhengchao Chen4,Han Wang1,2,Leiku Yang1,2
1.Key Laboratory of State Bureau of Surveying and mapping of Mine Spatial Information Technology, Henan Poly-technic University, Jiaozuo 454003, China
2.School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
3.Jiangsu Geologic Surveying and Mapping Institute, Nanjing 210000, China
4.Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
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摘要:

针对不同成像机理的光学与雷达遥感数据协同应用于地表信息提取瓶颈问题,提出了一种基于地形信息的光学与雷达数据协同分类方法。首先利用InSAR测量技术从Radarsat-2数据中提取DEM地形信息,然后构建基于地形信息的Landsat光学数据和Radarsat-2雷达数据的不同特征集输入模型,最后通过随机样本选取构建随机森林(Random Forest,RF)、支持向量机(Support Vector Machine, SVM)和决策树(Decision Tree,DT)分类算法模型提取地表信息。结果表明:①针对不同特征协同策略,在随机选取10%训练样本时,Radarsat-2干涉提取DEM与Landsat数据集提取精度优于ASTER GDEM与光学影像协同策略;②针对不同地表信息提取算法模型,通过50次随机选取训练样本构建模型评价分类精度,验证RF算法的鲁棒性和提取精度都要优于DT算法和SVM算法。研究充分利用光学和雷达遥感的优势信息,为光学和雷达遥感协同地表信息提取提供新的思路。

关键词: 随机森林LandsatRadarsat?2协同分类DEM    
Abstract:

Remote Sensing Data (RSD) and corresponding information extraction technologies are widely used in urban planning, ecological environment modeling, change detection, etc. Optical and radar remote sensing data, due to different imaging mechanisms, have complementary advantages for information extraction and applications. In order to improve the limitations of combining optical and SAR data for land surface information extraction. Strategies for Land Use and Land Cover (LUCC) classification based on collaborative processing optical, SAR RSD and topography information was proposed. Firstly, Digital Elevation Model (DEM) information was extracted from multi-temporal Radarsat-2 SAR images using InSAR technique. Then integrated model was constructed for information extraction based on inputs from topography data, Landsat optical RSD, Radarsat-2 SAR RSD. Finally, LULC information was extracted by random sample selection and machine learning algorithms (e.g. Random Forest (RF) ensemble learning method, Support Vector Machine (SVM) and Decision Tree (DT)). The results demonstrate that (1) when 10% training sample was selected, advantages come from combination of DEM extracted from Radarsat-2 SAR and Landsat data compared with combination of ASTER GDEM and the corresponding optical dataset; (2) Comparison results among different algorithm models by averaging 50 times classification accuracy of each model, demonstrate the robust and advantage of RF than DT and SVM. In this research, the combination advantage of optical and SAR remotely sensed data are explored, which can provide a new approach for making full use of optical and SAR data in the process of LULC classification.

Key words: Random Forest    Landsat    Radarsat-2    Collaborative classification    DEM
收稿日期: 2018-08-12 出版日期: 2020-03-23
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(41601450);重点研发与推广专项(科技攻关/社会发展:182102310860),河南理工大学博士基金(B2015-20)
通讯作者: 韩瑞梅     E-mail: Lipei@hpu.edu.cn;hrm@hpu.edu.cn
作者简介: 刘 培(1985-),男,河南许昌人,博士,副教授,主要从事资源环境遥感、数据挖掘与模式识别研究。E?mail: Lipei@hpu.edu.cn
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引用本文:

刘培,余志远,马威,韩瑞梅,陈正超,王涵,杨磊库. 基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究[J]. 遥感技术与应用, 2019, 34(6): 1269-1275.

Pei Liu,Zhiyuan Yu,Wei Ma,Ruimei Han,Zhengchao Chen,Han Wang,Leiku Yang. Remotely Sensed Data Classification by Collaborative Processing of Landsat, Radarsat-2 and Topography Information. Remote Sensing Technology and Application, 2019, 34(6): 1269-1275.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1269        http://www.rsta.ac.cn/CN/Y2019/V34/I6/1269

图1  研究区原始SAR与光学影像
图2  数据处理流程
图3  D-InSAR干涉生成研究区DEM数据
特征组合方案特征信息分类方法
AETM+ 光谱特征RFSVMDT
BETM+ 光谱特征 + InSAR DEMRFSVMDT
CETM+ 光谱特征 + ASTER GDEMRFSVMDT
表1  光学与雷达遥感数据协同分类方案
评定指标方案A方案B方案C
RFSVMDTRFSVMDTRFSVMDT
精度/%96.882.2695.6197.7483.6594.8297.5483.5596.01
Kappa0.958 80.782 50.944 00.970 90.798 40.934 40.968 30.797 30.948 9
表2  不同方案下不同分类方法精度评定
图4  不同方案下RF分类结果
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