Data fusion for the detection and reconstruction of buildings

Abstract

Research in detection and reconstruction of man-made objects from aerial images has made significant progress in the past two or three years. Two important reasons for that are: (1) data fusion of different sources provides more information for the ill-posed image analysis processes and (2) more sophisticated algorithms are developed which apply grouping and reasoning processes using a model of the object class of interest.

This paper presents an algorithm for automatic detection and reconstruction of buildings using height and image data. A given Digital Height Model (DHM), in the experiments computed by automatic stereo matching, is used to focus attention on regions where buildings are expected. Detection relies on the heuristic that buildings are represented in a DHM by regions with local height maxima. Object contours of buildings can be modelled by straight lines. Therefore, three-dimensional line segments are extracted from the image pair by stereo matching of grey-value edges. Again, the DHM is used to provide approximate parallaxes for the line segments. A building can be reconstructed by matching these observed three-dimensional lines to the lines of a model of the building. Position and shape of a building is estimated by minimizing the distances between the observed lines and the corresponding lines of a parameterized building model. The resulting error of least squares estimation provides a measure on how good the observed lines fit to the model. Thus it can be used to evaluate the result of the reconstruction. To have a second quality check the extracted roof regions of a building in a stereo image pair are matched by area based matching.