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DATA FUSION FOR CLASSIFICATION AND OBJECT EXTRACTION

This paper describes the fusion of information extracted from multispectral digital aerial images for land use classification. The proposed approach integrates spectral classification techniques and spatial information. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and NIR channels and form the basis for information extraction. Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model for a refined classification. The digital surface model is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature based points of interest we are able to generate a dense digital surface model by a dense matching procedure. This approach produces the desired land use classification results and exploits the high redundancy of the source data set to automatically perform after a short interactive training phase.