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:: FUSION OF OPTICAL AND INSAR FEATURES FOR BUILDING RECOGNITION IN URBAN AREAS J. D. Wegner, A. Thiele, U. Soergel

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 170 abed Figure 1. Flat-roofed (a) and gable-roofed (c) building in optical image overlaid with corresponding regions after segmentation (b,c) countries rooftops look usually grey, reddish or brownish but almost never green. Roof types can roughly be subdivided into flat roofs and gable-roofs. Flat roofs coincide often with rather homogeneous image regions (Fig. la) while gable-roofs sometimes appear less homogeneous. Chimneys and shadows cast by chimneys may further complicate roof extraction if homogeneous planes are fit to roofs (Fig. 1 c,d). Due to similar colour of adjacent roof and street regions, such entities are sometimes hard to be told apart even for human interpreters (Fig. 1 a,b). In this work the focus is on fusion of building primitive hypotheses delivered by approaches from the literature, tailored to the specific constraints that are determined by the particularities of the optical and microwave realm, respectively. With respect to the visible domain, a robust model-based roof detection approach introduced in Mueller and Zaum (2005), known to deliver good results, was used. It is based on an initial region growing step yielding homogeneous segments. As a consequence of the previously outlined diverse appearance of building roofs in optical imagery, such segmentation may sometimes lead to suboptimal results if contrast between roof regions and adjacent regions is very low (Fig. la). Thus, the region growing step can lead to erroneous roof segments (Fig. lb). Gable-roofs usually split up into at least two segments if they are not oriented along the sun illumination direction (Fig. 1. c,d). Sometimes, gable-roofs may split up into even more than two segments and only parts are evaluated as roof regions. In such cases, the introduction of building hints from SAR data can highly improve building detection. 2.2 Feature Extraction The building roof extraction approach consists of a low-level and a subsequent high-level image processing step (Mueller and Zaum, 2005). The low-level step includes transformation of the RGB image to HSI (Hue Saturation Intensity) representation, a segmentation of building hypotheses in the intensity image and the application of morphological operators in order to close small holes. Region growing, initialized with regularly distributed seed points on a grid, is used as image segmentation method. Seed points that fall into a grid cell which either consists of shadow or features a greenish hue value are erased and no region growing is conducted. Adjacent roof regions having a significant shadow region next to them are merged. This step is important for gable-roofed buildings because sometimes the roof is split at the roof ridge due to different illumination of the two roof parts. However, gable-roofs that were split up into more than two segments are not merged to one single segment which is the main reason for undetected buildings later-on in the process. Features are extracted for each roof hypothesis in order to prepare for classification. Four different feature types are used, based on geometry, shape, radiometry, and structure. Geometric features are the region size and its perimeter. The shape of a building region is described by its compactness and length. Right angles, distinguishing roofs from trees in the real world, are not used as a shape feature since the region growing step may lead to segments that are not rectangular although they represent roofs (Fig. lb). Radiometry is used in order to sort out regions with a high percentage of green pixels. Structural features are for example neighbouring building regions and shadows cast by the potential building. Shadows are good hints for elevated objects. In order to not take into account shadows cast by trees, only shadows with relatively straight borders are considered as belonging to buildings. Finally, a classification based on the previously determined feature vector takes place (see chapter 4.2 for details). All necessary evaluation intervals and thresholds were learned from manually classified training regions. 3. ANALYSIS OF INSAR DATA 3.1 Appearance of Buildings The appearance of buildings in InSAR data is characterized by the oblique illumination of the scene and therefore the image projection in slant range geometry. Furthermore, it depends on sensor parameters, on properties of the imaged object itself, and on the object’s direct environment. In Fig. 2 an example of flat-roofed buildings in optical (Fig. 2a) and InSAR data (Fig. 2 b,d) is given. The appearance of different building types and effects that occur if the scene is illuminated from two orthogonal flight directions have been comprehensively discussed in Thiele et al. (2007 and 2008). The magnitude profile of a building is typically a sequence of areas of various signal properties: layover, corner reflector between ground and building wall, roof, and finally radar shadow (Fig. 2c). The layover area is the building signal situated the closest to the sensor in the image because its distance is the shortest. It usually appears bright due to superposition of backscatter from ground, façade, and roof. The layover area ends at the bright so-called corner reflector line. This salient feature is caused by double-bounce reflection at a dihedral corner reflector spanned by ground and wall along the building. This line coincides with a part of the building footprint and can be distinguished from other lines of bright scattering using the InSAR phases (see Fig. 2d and profile in Fig. 2e). The single backscatter signal of the building roof is either included in the layover mixture or scattered away from

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