DOI: 10.1016/j.jag.2016.06.008
Scopus记录号: 2-s2.0-84997666811
论文题名: High resolution multisensor fusion of SAR, optical and LiDAR data based on crisp vs. fuzzy and feature vs. decision ensemble systems
作者: Bigdeli B ; , Pahlavani P
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 126
结束页码: 136
语种: 英语
英文关键词: LiDAR
; Multisensor fusion
; Optical data
; SAR
Scopus关键词: decision making
; fuzzy mathematics
; image classification
; lidar
; radar imagery
; remote sensing
; support vector machine
; synthetic aperture radar
; California
; San Francisco [California]
; United States
英文摘要: Synthetic Aperture Radar (SAR) data are of high interest for different applications in remote sensing specially land cover classification. SAR imaging is independent of solar illumination and weather conditions. It can even penetrate some of the Earth's surface materials to return information about subsurface features. However, the response of radar is more a function of geometry and structure than a surface reflection occurs in optical images. In addition, the backscatter of objects in the microwave range depends on the frequency of the band used, and the grey values in SAR images are different from the usual assumption of the spectral reflectance of the Earth's surface. Consequently, SAR imaging is often used as a complementary technique to traditional optical remote sensing. This study presents different ensemble systems for multisensor fusion of SAR, multispectral and LiDAR data. First, in decision ensemble system, after extraction and selection of proper features from each data, crisp SVM (Support Vector Machine) and Fuzzy KNN (K Nearest Neighbor) are utilized on each feature space. Finally Bayesian Theory is applied to fuse SVMs when Decision Template (DT) and Dempster Shafer (DS) are applied as fuzzy decision fusion methods on KNNs. Second, in feature ensemble system, features from all data are applied on a cube. Then classifications were performed by SVM and FKNN as crisp and fuzzy decision making system respectively. A co-registered TerrraSAR-X, WorldView-2 and LiDAR data set form San Francisco of USA was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with different sensor improves classification results for most of the classes. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80062
Appears in Collections: 气候变化事实与影响
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作者单位: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, North Kargar Street, P.O. Box: 11155-4563, Tehran, Iran
Recommended Citation:
Bigdeli B,, Pahlavani P. High resolution multisensor fusion of SAR, optical and LiDAR data based on crisp vs. fuzzy and feature vs. decision ensemble systems[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52