Fusion of colour infrared imagery and airborne laser scanning data in the automatic classification of urban environments
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The fusion of multi-sensor data important in the automatic classification of urban environments. In this study, the additional channel concept is used to fuse airbone laser scanning (ALS) data with a colour infrared (CIR) image acquired for a typical urban scene. To facilitate the co-registration of the two data sets is generated a CIR ortho-image using the digital surface model derived from the ALS data. This effectively integrates the geometric and multi-spectral information sources. In order to incorporate context analysis in the feature extraction, the feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied after an initial clustering procedure. Shadow effects present in the fused imagery are subsequently eliminated. Kappa related measures as well as accuracy parameters derived from Kullback-Leiber information on multinormal distributions are estimated. For comparison purposes, use is made of Delaunay triangulation. It is demonstrated that the classification of urban scenes is significantly improved by fusing the multi-spectral and geometric data sets.
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