Detecting blind building façades from highly overlapping wide angle aerial imagery

Abstract : This paper deals with the identification of blind building façades, i.e. façades which have no openings, in wide angle aerial images with a decimeter pixel size, acquired by nadir looking cameras. This blindness characterization is in general crucial for real estate estimation and has, at least in France, a particular importance on the evaluation of legal permission of constructing on a parcel due to local urban planning schemes. We assume that we have at our disposal an aerial survey with a relatively high stereo overlap along-track and across-track and a 3D city model of LoD 1, that can have been generated with the input images. The 3D model is textured with the aerial imagery by taking into account the 3D occlusions and by selecting for each façade the best available resolution texture seeing the whole façade. We then parse all 3D façades textures by looking for evidence of openings (windows or doors). This evidence is characterized by a comprehensive set of basic radiometric and geometrical features. The blindness prognostic is then elaborated through an (SVM) supervised classification. Despite the relatively low resolution of the images, we reach a classification accuracy of around 85% on decimeter resolution imagery with 60 Â 40% stereo overlap. On the one hand, we show that the results are very sensitive to the texturing resampling process and to vegetation presence on façade textures. On the other hand, the most relevant features for our classification framework are related to texture uniformity and horizontal aspect and to the maximal contrast of the opening detections. We conclude that standard aerial imagery used to build 3D city models can also be exploited to some extent and at no additional cost for facade blindness characterisation.
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Jean-Pascal Burochin, Bruno Vallet, Mathieu Brédif, Clément Mallet, Thomas Brosset, et al.. Detecting blind building façades from highly overlapping wide angle aerial imagery. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2014, 96, pp.193 - 209. ⟨http://www.sciencedirect.com/science/article/pii/S0924271614001944⟩. ⟨10.1016/j.isprsjprs.2014.07.011⟩. ⟨hal-01559518⟩

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