ADABOOST-BASED FEATURE RELEVANCE ASSESSMENT in FUSING LIDAR and IMAGE DATA for CLASSIFICATION of TREES and VEHICLES in URBAN SCENES

Keyphrases

• LiDAR 100%
• AdaBoost 100%
• Relevance Assessment 100%
• Feature Relevance 100%
• Image Dataset 50%
• Classification Results 33%
• Image Point 33%
• Co-registration 33%
• Point Cloud Data 33%
• AdaBoost Classifier 33%
• Planarity 16%
• Comparative Analysis 16%
• Statistical Features 16%
• Single Classifier 16%
• Feature Selection 16%
• Urban Scene 16%
• Tree Cover 16%
• Orientation Parameters 16%
• Back-projection 16%
• LiDAR Data 16%
• LiDAR Intensity 16%
• Spatial Contextual Information 16%
• Spatial Statistical Method 16%
• Radiometric Characteristics 16%
• Pixel Points 16%
• Contribution Ratio 16%
• Raster Format 16%
• Classification Measures 16%
• Consistent Feature 16%
• Random Forest Classifier 16%
• Reliable Features 16%

Computer Science

• Point Cloud 100%
• classification result 100%
• Combined Classifier 50%
• Feature Selection 50%
• Backprojection 50%
• Random Forest Classifier 50%
• Comparative Analysis 50%
• Context Information 50%
• Feature Extraction 50%
• Entropy 50%

Earth and Planetary Sciences

• Optical Radar 100%
• Comparative Analysis 50%
• Entropy 50%