Regionwise Classification of Building Facade Images

In recent years, the classification task of building facade images receives a great deal of attention in the photogrammetry community. In this paper, we present an approach for regionwise classification using an efficient randomized decision forest classifier and local features. A conditional random field is then introduced to enforce spatial consistency between neighboring regions. Experimental results are provided to illustrate the performance of the proposed methods using image from eTRIMS database, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window. This contribution was selected in a double blind review process to be published within the Lecture Notes in Computer Science series (Springer-Verlag, Heidelberg). Photogrammetric Image Analysis Volume Editors: Stilla U, Rottensteiner F, Mayer H, Jutzi B, Butenuth M LNCS Volume: 6952 Series Editors: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Kobsa A, Mattern F, Mitchell JC, Naor M, Nierstrasz O, Pandu Rangan C, Steffen B, Sudan M, Terzopoulos D, Tygar D, Weikum G ISSN: 0302-9743 The article is accessible online through www.springerlink.com. 43 In: Stilla U et al (Eds) PIA11. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (3/W22)