Knudsen, Thomas

Nielsen, Allan Aasbjerg

In recent publications, numerous methods for automated reconstruction/registration of buildings from stereo imagery have appeared (cf. e.g. Niederöst, 2003; Süweg, 2003). A common characteristic of these methods is the requirement of an existing approximate position (AP) of the building to be registered. In the present work, we develop a method for producing such APs based on sets of true colour (RGB) or colourinfrared (CIR) aerial imagery in combination with prior registrations from a GIS database. The primary intention with the work is to support the automated update of the GIS database. At the current stage of the project, the main goal is to produce a dataset where as much as possible of the “non building” area is masked out

Online Research Database In Technology

Detection of buildings through multivariate analysis of spectral, textural, and shape based featuresThomas Knudsen National Survey and Cadastre DK, Geodetic Office, Rentemestervej 8, DK-2400 Copenhagen NV, thk@kms.dkAllan Aasbjerg Nielsen Techn. Univ. of Denmark, IMM, Richard Petersens Plads, Bldg. 321, DK-2800 Lyngby, aa@imm.dtu.dkINTRODUCTION: In recent publications, numerousmethods for automated reconstruction/registration ofbuildings from stereo imagery have appeared (cf. e.g.Niederöst, 2003; Süweg, 2003). A common characteris-tic of these methods is the requirement of an existing ap-proximate position (AP) of the building to be registered.In the present work, we develop a method for producingsuch APs based on sets of true colour (RGB) or colour-infrared (CIR) aerial imagery in combination with priorregistrations from a GIS database.The primary intention with the work is to support theautomated update of the GIS database. At the currentstage of the project, the main goal is to produce a datasetwhere as much as possible of the “non building” area ismasked out.METHOD: The prior registrations from the GIS databaseare used to derive a training dataset for a multivariate clas-sifier. The training dataset is pulled from a post processedversion of the imagery, which is first segmented usingthe EDISON segmentation algorithm (Christoudias et al,2002). The original 3 channel dataset is then extendedto a 20 channel feature vector for each segment by com-puting a vegetation index and a set of shape and texturalparameters. The a priori assumption is that the vegeta-tion index and texture indices will help remove vegetatedbackground, while the shape based indices will help re-move road segments.A multipart detection algorithm is utilized; the first partdiscriminates buildings from background by thresholdingin the Mahalanobis distance from each feature vector tothe set of feature vectors in the training set; the secondpart is a tree based method (cf. Breiman et al, 1984). Thesynergistic use of two (and potentially more) methods isdesirable as both methods are at the limit of their descrip-tive power in our case: the Mahalanobis metric is affectedby the fact that some components of the feature vector arefar from being normal distributed; the tree based methodis limited due to the small size of the input data set.RESULTS & DISCUSSION: In general, the tree classi-fication routine matches the input data very well, with amisclassification below 3% in all cases. The complexityof the datasets is, however reflected in the number of ter-minal nodes of the classification trees, which falls in therange 20–50, with the CIR cases in the lower end. This(expected) tendency of better separability in the CIR caseis also reflected in the Mahalanobis discrimination step.A sample result is shown in the figure. The upper panelshows the segmented image, with the training datasetoverlaid in white (the sample is from a CIR photo, so veg-etation is shown in red). The lower panel shows the re-sult of the algorithm. The assumption that vegetation androads could be detected from vegetation index and shaperespectively, seems to hold. The major task for furtherwork is now to get a more reliable evaluation of the exist-ing buildings, allowing us to decide directly whether theprior registrations should be kept or should be updated.REFERENCES: L. Breiman, J.H. Friedman, R.A. Olshen,C.J. Stone: Classification and Regression Trees. Monterey:Wadsworth and Brooks/Cole, 1984Chris M. Christoudias, Bogdan Georgescu, and Peter Meer:Synergism in low level vision. In 16th International Conferenceon Pattern Recognition. Track 1: Computer Vision and Robotics,Quebec City, Canada, volume IV, pages 150–155, August 2002.Markus Niederöst. Detection and Reconstruction of Buildingsfor Automated Map Updating. PhD thesis, Institut für Geodäsieund Photogrammetrie, Eidgenössische Technische HochschuleZürich, ETH Hönggerberg, 2003.Ildikó Süweg. Reconstruction of 3D Building Models fromAerial Images and Maps. Number 53 in Publications onGeodesy. Netherlands Geodetic Commision, Delft, the Nether-lands, May 2003. ISBN 90 6132 280 4.

Detection of buildings through multivariate analysis of spectral, textural, and shape based features

http://orbit.dtu.dk/ws/files/2756032/imm3168.pdf

Detection of buildings through multivariate analysis of spectral, textural, and shape based features

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology