Surface Gradient Approach for Occlusion Detection Based on Triangulated Irregular Network for True Orthophoto Generation

Aerial images of urban areas have been used as base information for a diversity of applications. Considering the great quantity of tall buildings in these areas, it is important to have a method to automatically generate a product called true orthophoto mosaic, which represents all objects above the ground (buildings, bridges, etc.) in their true location. However, to create a true orthophoto, it is necessary to consider the occlusions caused by the surface height variation and to compensate for the lack of information using adjacent aerial images. The automatic occlusion detection is the bottleneck during the true orthophoto mosaic generation. The main aim of this paper is to introduce a new approach for occlusion detection – the surface-gradient-based method (SGBM) applied to a triangulated irregular network (TIN) representation. The originality of the SGBM is the occlusion detection principle, which is based on the concept of surface gradient behavior analysis over a TIN surface. The current methods interpolate a point cloud into a gridded digital surface model, which can introduce artifacts to the representation. The SGBM represents the surface as a TIN-based solid by taking into account the Delaunay constraint in the original point cloud, avoiding the interpolation step. The occlusions are then compensated using specific cost functions and refined via color blending. Experiments were performed and the results were assessed by using quality indicators (completeness), the consistency of orthoimage mosaic, and the time of processing. Experimental results demonstrated the feasibility of the SGBM for occlusion detection in the true orthophoto generation.

[1]  Tee-Ann Teo,et al.  Occlusion-Compensated True Orthorectification For High-Resolution Satellite Images , 2007 .

[3]  P. Wolf,et al.  Elements of Photogrammetry(with Applications in GIS) , 2000 .

[4]  Wei Wang,et al.  Building Occlusion Detection From Ghost Images , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[6]  Weirong Chen,et al.  A comprehensive study on urban true orthorectification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Manchun Li,et al.  A methodology for true orthorectification of large-scale urban aerial images and automatic detection of building occlusions using digital surface model , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[8]  Morten Ødegaard Nielsen.  True orthophoto generation , 2004 .

[9]  Changjae Kim,et al.  New Methodologies for True Orthophoto Generation , 2007 .

[10]  Daniela Oreni,et al.  True-orthophoto generation from UAV images: Implementation of a combined photogrammetric and computer vision approach , 2014 .

[11]  David F. Rogers,et al.  Procedural Elements for Computer Graphics , 1984 .

[12]  A. Habib,et al.  PLANAR CONSTRAINTS FOR AN IMPROVED UAV-IMAGE-BASED DENSE POINT CLOUD GENERATION , 2015 .

[13]  H. Hirschmüller.  Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.

[14]  Aluir Porfírio Dal Poz,et al.  Rectilinear building roof contour extraction based on snakes and dynamic programming , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Christian Thom,et al.  SURFACE RECONSTRUCTION IN URBAN AREAS FROM MULTIPLE VIEWS WITH AERIAL DIGITAL FRAME CAMERAS , 2000 .

[16]  Jiann-Yeou Rau,et al.  True orthophoto generation of built-up areas using multi-view images , 2002 .

[17]  W. Schickler,et al.  OPERATIONAL PROCEDURE FOR AUTOMATIC TRUE ORTHOPHOTO GENERATION , 2003 .

[18]  Mauricio Galo,et al.  OCCLUSION DETECTION BY HEIGHT GRADIENT FOR TRUE ORTHOPHOTO GENERATION, USING LIDAR DATA , 2013 .

[19]  Eunju Kwak,et al.  Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle , 2014 .

[20]  Ayman Habib,et al.  A NEW APPROACH FOR SEGMENTATION-BASED TEXTURING OF LASER SCANNING DATA , 2015 .

[21]  Uwe Stilla,et al.  Building boundary improvement for true orthophoto generation by fusing airborne LiDAR data , 2011, 2011 Joint Urban Remote Sensing Event.

[22]  E. Mikhail,et al.  Introduction to modern photogrammetry , 2001 .

[23]  Josef Jansa,et al.  THE GENERATION OF TRUE ORTHOPHOTOS USING A 3D BUILDING MODEL IN CONJUNCTION WITH A CONVENTIONAL DTM , 1998 .

[24]  Christian Heipke,et al.  EMPIRICAL EVALUATION OF AUTOMATICALLY EXTRACTED ROAD AXES , 1998 .

[25]  Mauricio Galo,et al.  Height-Gradient-Based Method for Occlusion Detection in True Orthophoto Generation , 2015, IEEE Geoscience and Remote Sensing Letters.

[26]  W. Marsden.  I and J , 2012 .

[27]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[28]  Edward H. Adelson,et al.  A multiresolution spline with application to image mosaics , 1983, TOGS.

[29]  Aloysius Wehr,et al.  Airborne laser scanning—an introduction and overview , 1999 .

[30]  James J. Little,et al.  Triangulated Irregular Network , 2017, Encyclopedia of GIS.

[31]  A. Habib,et al.  HEIGHT GRADIENT APPROACH FOR OCCLUSION DETECTION IN UAV IMAGERY , 2015 .

[32]  Fuling Bian,et al.  Occlusion detection analysis based on two different DSM models in true orthophoto generation , 2008, Geoinformatics.