CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: COMPARISON OF METHODS FOR AUTOMATED BUILDING EXTRACTION FROM HIGH RESOLUTION IMAGE DATA G. Vozikis

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 2.1.1 Pre-Processing This step comprises the procedures from orientation of the input data up to the DSM (Digital Surface Model) creation. For VHR satellite imagery the orientation approach is based on the RFM (Rational Function Model) (Vozikis et al., 2003). When dealing with aerial imagery it is made use of GPS/INS information in order perform direct georeferencing, and thus automated image triangulation (Scholten and Gwinner, 2003). The DSM extraction is performed by automated correlation procedures, which nowadays are very mature and produce very good results. 2.1.2 nDSM Creation The goal is to derive the DTM (Digital Terrain Model) from the DSM and subtract it from the DSM in order to produce the so- called nDSM (normalized Digital Surface Model). This way all extruding objects in the data set (including buildings) stand on elevation height 0 (Figure 2). Figure 2: DSM, DTM and nDSM. 2.1.3 Building Detection (Seeding) This crucial step deals with the identification of potential building candidates in the data sets (=determination of seed points inside buildings). It is proposed to perform 2 statistical analyses. First, perform a thresholding in the nDSM and filter out all objects that are not taller than a certain height, and second, perform texture analysis in the image data to keep only roof-similar regions in the data set (Vozikis, 2004). Figure 3: Computation of seed points (red asterisks) inside potential building candidates by height-thresholding and texture filtering. 2.1.4 Building Extraction By applying the Hough Transformation (to an image of gradient or of contours) the geometric properties of the buildings (building edges and comers) are extracted. Our approach is based on a stepwise, iterative Hough Transformation in combination with an adaptive region growing algorithm (Vozikis and Jansa, 2008). The general idea is to transform the information in the image (feature space) into a parameter space and apply there an analysis. It is a technique for isolating features that share common characteristics. The classical Hough transformation is used to detect lines, circles, ellipses etc., whereas the generalized form can be used to detect features that cannot easily be described in an analytical way. The mathematical analysis of the Hough Transformation is described in detail in Gonzalez and Woods (1992). Briefly it can be described as follows: p = xcos(#)~ vsin(#) (1) where p is the perpendicular distance of a line from the origin and 0 the angle (in the range 0 to 7r) as illustrated in Figure 4. To apply this function on the whole image, Equation 1 can be extended as shown in Equation 2. H(e,p)= \ jF(x,y)S(p- xcos(#)-ysin(#))<ir£fr (2) where 8 is the Dirac delta-function. Each point (x,y) in the original image F(x,y) is transformed into a sinusoid p = xcos(0) - ysin(0). 84

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