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Accession Number ADA567217
Title Urban Classification Techniques Using the Fusion of LiDAR and Spectral Data.
Publication Date Sep 2012
Media Count 85p
Personal Author J. E. Mesina
Abstract Combining different types of data from varying sensors has the potential to be more accurate than a single sensor. This research fused airborne LiDAR data and WorldView-2 (WV-2) multispectral imagery (MSI) data to create an improved classification image of urban San Francisco, California. A decision tree scenario was created by extracting features from the LiDAR as well as Normalized Difference Vegetation Index (NDVI) from the multispectral data. Raster masks were created using these features and were processed as decision tree nodes resulting in seven classifications. Twelve regions of interest were created, categorized, and then applied to the previous seven classifications via maximum likelihood classification. The resulting classification images were then combined. A multispectral classification image using the same ROIs also was created for comparison. The fused classification image did a better job of preserving urban geometries than MSI data alone, and it suffered less from shadow anomalies. The fused results, however, were not as accurate in differentiating trees from grasses as using only spectral results. Overall, the fused LiDAR and MSI classification performed better than the MSI classification alone, but further refinements to the decision tree scheme could probably be made to improve the final results.
Keywords Accuracy
Aerial photography
Airborne lidar
Data fusion
Decision trees
Feature extraction
Ikonos satellite
Image processing
Landsat satellite
Lidar data
Lidar(Light detection and ranging)
Mask creation
Maximum likelihood classification
Multi-source fusion
Multispectral imagery data
Multispectral processing
Ndvi(Normalized difference vegetation index)
Optical radar
Point cloud processing
Quick terrain modeler
Remote detection
Rule based systems
San francisco(California)
Satellite imagery
Satellite multispectral imagery
Sensor fusion
Shadow anomalies
Urban areas
Urban classification
Worldview-2 satellite

Source Agency Non Paid ADAS
NTIS Subject Category 88B - Information Systems
62 - Computers, Control & Information Theory
82B - Photographic Techniques & Equipment
63 - Detection & Countermeasures
99F - Physical & Theoretical Chemistry
48I - Cartography
Corporate Author Naval Postgraduate School, Monterey, CA. Dept. of Informational Sciences.
Document Type Thesis
Title Note Master's thesis.
NTIS Issue Number 1308
Contract Number N/A

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