基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究

doi:10.11873/j.issn.1004-0323.2019.6.1269

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 Liu^1,²(

),Zhiyuan Yu^1,^2,⁴,Wei Ma³,Ruimei Han^1,²(

),Zhengchao Chen⁴,Han Wang^1,²,Leiku Yang^1,²

^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; Landsat; Radarsat?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 或 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

表1 光学与雷达遥感数据协同分类方案

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

1	Melesse A M, Weng Q H, Thenkabail P S, et al. Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling[J]. Sensors, 2007, 7( 12): 3209- 3241.
2	Zhu Z, Woodcock C E, Rogan J, et al. Assessment of Spectral, Polarimetric, Temporal, and Spatial Dimensions for Urban and Peri-urban Land Cover Classification Using Landsat and SAR Data[J]. Remote Sensing of Environment, 2012, 117: 72- 82.
3	Du P J, Liu P, Xia J S, et al. Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples[J]. Remote Sensing, 2014, 6( 10): 9458- 9474.
4	Liu Pei, Wang Guangyan, Zou Youfeng, et al. Research of Information Integration and Classifier Ensemble for Land Cover Types Identification [J]. Remote Sensing Information, 2016( 4): 116- 121.
4	刘培, 王光彦, 邹友峰, 等. 遥感信息融合和分类器集成的地类高精度识别[J]. 遥感信息, 2016( 4): 116- 121].
5	Mou L, Zhu X, Vakalopoulou M, et al. Multi-temporal Very High Resolution from Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017: 1- 13.
6	Campos Taberner M, Romero Soriano A, Gatta C, et al. Processing of Extremely High-resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9( 12): 5547- 5559.
7	Vo A V, Truong Hong L, Laefer D F, et al. Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9( 12): 5560- 5575.
8	Liao W Z, Huang X, Frieke Van Coillie, et al. Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015. 8( 6): 2984- 2996.
9	Liu Lei, Zang Shuying, Na Xiaodong, et al. Wetland Mapping in the Zhalong Natural Reserve Using Optical and Radar Remotely Sensed Data and Ancillary Topographical Data[J]. Geography and Geo-Information Science, 2013, 29( 1): 36- 40.
9	刘蕾, 臧淑英, 那晓东, 等. 兼容光学雷达影像及地形辅助数据的扎龙湿地遥感分类[J]. 地理与地理信息科学, 2013, 29( 1): 36- 40.
10	Liu Pei, Du Peijun, Tan Kun. A Novel Remotely Sensed Image Classification based on Ensemble Learning and Feature Integration [J]. Journal of Infrared and Millimeter Waves, 2014, 33( 3): 311- 317.
10	刘培, 杜培军, 谭琨. 一种基于集成学习和特征融合的遥感影像分类新方法[J]. 红外与毫米波学报, 2014, 33( 3): 311- 317.
11	Ma Huijuan, Gao Xiaohong, Gu Xiaotian. Random Forest Classification of Landsat 8 Imagery for the Complex Terrain Area based on the Combination of Spectral, Topographic and Texture Information[J]. Journal of Geo-information Science, 2019, 21( 3): 359- 371.
11	马慧娟,高小红,谷晓天. 随机森林方法支持的复杂地形区土地利用/土地覆被分类研究[J]. 地球信息科学学报, 2019, 21( 3): 359- 371.
12	Xu Qiao, Zhang Xiao, Yu Shaohuai, et al. Multi-feature-based Classification Method Using Random Forest and Superpixels for Polarimetric SAR Images[J]. Journal of Remote Sensing, 2019, 23( 4): 685– 694.
12	徐乔, 张霄, 余绍淮, 等. 综合多特征的极化SAR图像随机森林分类算法[J]. 遥感学报, 2019, 23( 4): 685- 694.
13	He Yun, Huang Chong, Li He, et al. Land-cover Classification of Random Forest based on Sentinel-2A Image Feature Optimization[J]. Resources Science, 2019, 41( 5): 992- 1001.
13	何云, 黄翀, 李贺, 等. 基于Sentinel-2A影像特征优选的随机森林土地覆盖分类[J]. 资源科学, 2019, 41( 5): 992- 1001.
14	Du Yong, Research on Extraction of High-precision DEM based on InSAE Baseline Correction[D]. Beijing: Beijing Jiaotong University, 2010.
14	[杜勇,基于InSAR基线校正的高精度DEM提取研究[D]. 北京：北京交通大学, 2010.]
15	Ai Bin, Li Xia. An Analysis of Different INSAR Flattening Algorithms and Their Influence on DEM Accuracy [J]. Remote Sensing for Land and Resources， 2009, 21( 3): 12- 18.
15	艾彬,黎夏. InSAR平地相位去除算法及其对DEM精度影响研究[J]. 国土资源遥感, 2009, 21( 3): 12- 18.
16	Belgiu M, Drăguţ Lucian. Random Forest in Remote Sensing: A Review of Applications and Future Directions[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114: 24- 31.
17	Tang Wei, Zhou Zhihua. Bagging-based Selective Clusterer Ensemble [J]. Journal of Software, 2005, 16( 4): 496- 502.
17	唐伟,周志华. 基于 Bagging 的选择性聚类集成[J]. 软件学报, 2005, 16( 4): 496- 502.
18	Xiao Rong, Wang Jicheng, Sun Zhengxing, et al. An Incremental SVM Learning Algorithm α-ISVM [J]. Journal of Software, 2001, 12( 12): 1818- 1824.
18	萧嵘, 王继成, 孙正兴, 等. 一种SVM增量学习算法α—ISVM[J]. 软件学报, 2001, 12( 12): 1818- 1824.

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