Building detection by fusion of airborne laser scanner data and multi-spectral images: Performance evaluation and sensitivity analysis

In this paper, we describe the evaluation of a method for building detection by the Dempster-Shafer fusion of airborne laser scanner (ALS) data and multi-spectral images. For this purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic models for the probability mass assignments are validated and improved, and rules for tuning the parameters are discussed. The sensitivity of the results to the most important control parameters of the method is assessed. Further we evaluate the contributions of the individual cues used in the classification process to determine the quality of the results. Applying our method with a standard set of parameters on two different ALS data sets with a spacing of about 1 point/ in m(2), 95% of all buildings larger than 70 m(2) could be detected and 95% of all detected buildings larger than 70 m(2) were correct in both cases. Buildings smaller than 30 in m(2) could not be detected. The parameters used in the method have to be appropriately defined, but all except one (which must be determined in a training phase) can be determined from meaningful physical entities. Our research also shows that adding the multi-spectral images to the classification process improves the correctness of the results for small residential buildings by up to 20%. (c) 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.