Fusion of airborne LiDAR with multispectral SPOT 5 image for enhancement of feature extraction using dempster-shafer theory

Fusion of airborne LiDAR with multispectral SPOT 5 image for enhancement of feature extraction using dempster-shafer theory

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This paper presents an application of data-driven Dempster-Shafer theory (DST) of evidence to fuse multisensor data for land-cover feature extraction. Over the years, researchers have focused on DST for a variety of applications. However, less attention has been given to generate and interpret proba...

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Bibliographic Details
Main Authors: Idrees, M.O (Author), Latif, Z.A (Author), Pradhan, B. (Author), Saeidi, V. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2014
Subjects:
accuracy assessment
airborne sensing
Classification accuracy
Data mining
Dempster-Shafer theory
Dempster-Shafer theory (DST)
Digital surface models
digital terrain model
Extraction
Feature extraction
Fusion
Fusion reactions
Geographic information systems
GIS
image classification
land cover
lidar
Light detection and ranging
Light Detection and Ranging (LiDAR)
Multispectral satellite imagery
NDVI
Normalized difference vegetation index
Optical radar
Quantitative assessments
remote sensing
Remote sensing
satellite imagery
Satellite Pour l'Observation de la Terre
SPOT
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