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Urban Classification Techniques Using the Fusion of LiDAR and Spectral Data

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.