Automatic Registration of Oblique Aerial Images with Cadastral Maps

In recent years, oblique aerial images of urban regions have become increasingly popular for 3D city modeling, texturing, and various cadastral applications. In contrast to images taken vertically to the ground, they provide information on building heights, appearance of facades, and terrain elevation. Despite their widespread availability for many cities, the processing pipeline for oblique images is not fully automatic yet. Especially the process of precisely registering oblique images with map vector data can be a tedious manual process. We address this problem with a registration approach for oblique aerial images that is fully automatic and robust against discrepancies between map and image data. As input, it merely requires a cadastral map and an arbitrary number of oblique images. Besides rough initial registrations usually available from GPS/INS measurements, no further information is required, in particular no information about the terrain elevation.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Yaser Sheikh,et al.  Geodetic Alignment of Aerial Video Frames , 2003 .

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[4]  Agnès Desolneux,et al.  Vanishing Point Detection without Any A Priori Information , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  David N. Fogel,et al.  Image Registration using Multiquadric Functions, the Finite Element Method, Bivariate Mapping Polynomials and Thin Plate Spline (96-1) , 1996 .

[6]  Xiaqing Wu,et al.  Automatic alignment of large-scale aerial rasters to road-maps , 2007, GIS.

[7]  Eyal Ofek,et al.  Validation of vector data using oblique images , 2008, GIS '08.

[8]  Andrew Zisserman,et al.  Metric rectification for perspective images of planes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[9]  H. Opower.  Multiple view geometry in computer vision , 2002 .

[10]  Dani Lischinski,et al.  Deep photo: model-based photograph enhancement and viewing , 2008, SIGGRAPH 2008.

[11]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[12]  Görres Grenzdörffer,et al.  Photogrammetric image acquisition and image analysis of oblique imagery , 2008 .

[13]  Christian Früh,et al.  Automated texture mapping of 3D city models with oblique aerial imagery , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[14]  Eberhard G Ulch.  Extraction of 3d Objects from Aerial Photographs 1 1. Motivation , .

[15]  A. P. Nyaruhuma,et al.  Incorporating scene constraints into the triangulation of airborne oblique images , 2009 .

[16]  Daniel G. Aliaga,et al.  Building reconstruction using manhattan-world grammars , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  D. Stallmann,et al.  SYSTEM CALIBRATION FOR DIRECT GEOREFERENCING , 2002 .

[18]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[19]  C. Lemmen,et al.  Pictometry : potentials for land administration , 2007 .

[20]  Avideh Zakhor,et al.  Automatic registration of aerial imagery with untextured 3D LiDAR models , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  M. Gerke.  Dense matching in high resolution oblique airborne images , 2009 .

[22]  Lu Wang,et al.  A robust approach for automatic registration of aerial images with untextured aerial LiDAR data , 2009, CVPR.

[23]  Uwe Stilla,et al.  Texture mapping of 3D building models with oblique direct geo-referenced airborne IRimage sequences , 2009 .

[24]  T. Läbe.  AUTOMATIC RELATIVE ORIENTATION OF IMAGES , 2006 .

[25]  Juan B. Mena,et al.  State of the art on automatic road extraction for GIS update: a novel classification , 2003, Pattern Recognit. Lett..