Detection of buildings through multivariate analysis of spectral, textural, and shape based features

In order to facilitate the update of the building theme in photogrammetrically derived GIS databases, we investigate spectra, textural, and shape features of areas of aerial photos previously registered as buildings. In following steps, these features are used in a classification tree characterisation of the entire photo, and a simple classification routine (minimum Mahalanobis distance). The classification results are to he used for (1) verification of the GIS database and (2) change detection for subsequent update work. The classification trees show to be hard to prune (generalize) without large loss of precision. This indicates that the problem at hand is far from trivial: the building and background classes are hard to separate. Hence, the method described here cannot stand alone: additional algorithms focusing on different aspects of the problem (and finally combined in a fusion step) could be one way to go; another, technically more promising, but economically less viable, would be to enter high resolution height data, derived from photogrammetry or laser scanning, into the input data set