VERIFICATION OF 3D BUILDING MODELS USING MUTUAL INFORMATION IN AIRBORNE OBLIQUE IMAGES

Abstract. This paper describes a method for automatic verification of 3D building models using airborne oblique images. The problem being tackled is identifying buildings that are demolished or changed since the models were constructed or identifying wrong models using the images. The models verified are of CityGML LOD2 or higher since their edges are expected to coincide with actual building edges. The verification approach is based on information theory. Corresponding variables between building models and oblique images are used for deriving mutual information for individual edges, faces or whole buildings, and combined for all perspective images available for the building. The wireframe model edges are projected to images and verified using low level image features – the image pixel gradient directions. A building part is only checked against images in which it may be visible. The method has been tested with models constructed using laser points against Pictometry images that are available for most cities of Europe and may be publically viewed in the so called Birds Eye view of the Microsoft Bing Maps. Results are that nearly all buildings are correctly categorised as existing or demolished. Because we now concentrate only on roofs we also used the method to test and compare results from nadir images. This comparison made clear that especially height errors in models can be more reliably detected in oblique images because of the tilted view. Besides overall building verification, results per individual edges can be used for improving the 3D building models.

[1]  George Vosselman,et al.  Reconstruction of 3D building models from aerial images and maps , 2003 .

[2]  J. Ponce,et al.  Accurate, Dense, and Robust Multi-View Stereopsis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  G. Petrie.  Systematic Oblique Aerial Photography Using Multiple Digital Frame Cameras , 2009 .

[5]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[6]  Yandong Wang,et al.  PICTOMETRY ' S PROPRIETARY AIRBORNE DIGITAL IMAGING SYSTEM AND ITS APPLICATION IN 3 D CITY MODELLING , 2008 .

[7]  George Vosselman,et al.  EVIDENCE OF WALLS IN OBLIQUE IMAGES FOR AUTOMATIC VERIFICATION OF BUILDINGS , 2010 .

[8]  George Vosselman,et al.  Quality analysis on 3D building models reconstructed from airborne laser scanning data , 2011 .

[9]  Ramakant Nevatia,et al.  Detecting changes in aerial views of man-made structures , 2000, Image Vis. Comput..

[10]  George Vosselman,et al.  Building Reconstruction by Target Based Graph Matching on Incomplete Laser Data: Analysis and Limitations , 2009, Sensors.

[11]  M. Gerke.  Using horizontal and vertical building structure to constrain indirect sensor orientation , 2011 .

[12]  George Vosselman,et al.  Mutual information based evaluation of 3D building models , 2002, Object recognition supported by user interaction for service robots.

[13]  Ryosuke Shibasaki,et al.  Building Change Detection Using 3-D Texture Model , 2008 .

[14]  Thomas Knudsen.  An Algorithm for Verication and Change Detection Between 3D Geospatial Databases and Aerial Images , 2007 .

[15]  Marc Pollefeys,et al.  Image based detection of geometric changes in urban environments , 2011, 2011 International Conference on Computer Vision.

[16]  A. P. Nyaruhuma,et al.  Verification of 2D building outlines using oblique airborne images , 2012 .

[17]  George Vosselman,et al.  Relational Matching , 1992, Lecture Notes in Computer Science.

[18]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.