Remote Sensing | |
Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification | |
Shezhou Luo3  Cheng Wang3  Xiaohuan Xi3  Hongcheng Zeng2  Dong Li3  Shaobo Xia3  Pinghua Wang3  Parth Sarathi Roy1  | |
[1] Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaFaculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada;Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; | |
关键词: LiDAR; hyperspectral image; land cover classification; data fusion; support vector machine; maximum likelihood classification; | |
DOI : 10.3390/rs8010003 | |
来源: mdpi | |
【 摘 要 】
Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images,
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190001141ZK.pdf | 3565KB | download |