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:: THEORETICAL ANALYSIS OF BUILDING HEIGHT ESTIMATION USING SPACEBORNE SAR-INTERFEROMETRY FOR RAPID MAPPING APPLICATIONS Stefan Hinz, Sarah Abelen

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 164 The reminder of this paper is organized as follows: Section 2 gives a brief review of state-of-the-art methods for delineating the 3D geometry of buildings from SAR images in general, eventually leading to a discussion of the boundary conditions of this study. The theoretical background for height estimation from across-track interferometry as well as error sources are compiled in Section 3, before Section 4 analyses the accuracy potential of deriving building heights under various given prerequisites. Finally, Section 5 draws conclusions in the light of the TanDEM-X mission and the results of this study. 2. 3D BUILDING GEOMETRY FROM SAR IMAGES 2.1. Overview Over the past decades, a large variety of approaches for deriving 3D building information from SAR images has been developed. According to underlying methods and used data the different methods can be roughly grouped into following categories: (a) height-from-shadow using mono- or multi-aspect data (b) fitting prismatic models based on statistical optimization (c) model-driven segmentation of pre-computed height data (d) height estimation supported by feature detection / matching (e) Exploiting layover areas in single or multiple InSAR pairs To keep the overview focused, we only refer to the original work of each of these groups. We are aware that numerous approaches have been developed meanwhile, which could be assigned to one or more of these groups. Ad a) Due to the oblique imaging geometry of SAR systems, buildings cause the well-known RADAR shadow, which basically corresponds to the occluded area at ground. As for conventional optical shape-from-shadow approaches, it only needs simple trigonometry to calculate the object height from the shadow boundary when knowing the sensor imaging geometry and assuming horizontal ground (similar for the layover area (Tupin, 2003)). A compilation of the corresponding formulae can be found, for instance, in (Sorgel et al. 2006). It is usually assumed that a shadow edge corresponds to a certain object edge, whose height is to be estimated. As only a few number of building edges can be matched to shadow edges for a specific viewing direction of the SAR, (Bolter & Leberl, 2000; Leberl & Bolter, 2001) generalize this approach to multi-aspect SAR and embed it into an iterative height estimation framework supported by InSAR cues. By this, building footprint and height are estimated simultaneously, yielding an accuracy of 1.5m - 2m for airborne SAR. Ad b) The concept described in (Quartulli & Datcu, 2001; 2003) models the geometry of buildings and geometric relations between adjacent buildings by a number of parameters (position, length, width, height, roof slope, distance etc.). After initialization of model instances in image space, the parameters are statistically optimized using amplitude, coherence and interferometric phase information from the images. While this kind of thorough object-oriented modeling helps to cope with heavy noise and image derogations, it limits the approach to a small number of building shapes, not speak about the computational complexity mandatory for parameter optimization. This might one of the reasons why the results cannot prove the general feasibility of the approach and no accuracy analysis has been carried out; whereas, the mathematical formulation is very elegant. Ad c) A purely data-driven strategy that complements the aforementioned approach is presented in (Gamba & Houshmand, 1999; Gamba et al., 2000). The procedure starts with the computation of the interferogram and derives level lines by segmenting it into height intervals. Level lines fulfilling certain shape constraints are selected as seed points to start a regiongrowing algorithm. This algorithm continues as long as segments can be added without exceeding a predefined threshold for co-planarity. The achieved accuracy using airborne C-band data is reported to be 2.5m for large industrial buildings. This method is in principle independent of the data source and can be applied to any kind of height models, as so for LIDAR-based height models (Gamba & Houshmand, 2000). Ad d) While the former extraction strategy infers the semantics of buildings purely based on the roof geometry, approaches following the spirit of (Sorgel et al., 2003; Tison et al., 2007) include hypotheses of buildings, building parts, and/or adjacent context objects (roads, vegetation, etc.) from the very beginning of processing. To this end, a supervised classification and/or feature detection is carried out before building reconstruction. This may contain areal objects but also linear features and spots indicating double bounces at building walls, which become especially prominent in high resolution SAR (Stilla, 2007). The cues provided by these hypotheses are then iteratively grouped and optimized together with the heights derived from InSAR data until reasonably shaped buildings are extracted or hypotheses are rejected. Due to generic processing of multiple cues, this concept is easily extended to multi-aspect SAR data. The reported accuracy yields again 2 - 3m for the airborne case. Ad e) The final group of approaches does not only include image features derived from SAR or InSAR data but models the complete interferometric phase profile for building walls and roofs (Thiele et al., 2007). Since vertical walls form layover areas as consequence of the oblique RADAR distance measurement, this kind of modelling implicitly contains the assumption that the main contribution of scattering in such layover areas is induced by building walls and not by clutter in front of the building or by the overlayed part of the roof. This approach can be generalized to SAR tomography (Reigber & Moreira, 2000; Fornaro et al., 2003) if more than one interferometric pair of the same viewing direction is available. (Zhu et al., 2008; 2009) show that deriving 3D information via tomographic analysis and statistical model selection can be adapted to pixelwise calculation of dense height maps of urban areas, thus linking the concepts of SAR tomography with Persistent Scatterer Interferometry (Ferretti et al., 2001; Kampes, 2006). These approaches are however in a preliminary stage so that a thorough accuracy analysis is not yet available. 2.2 Discussion While each of the approaches is characterized by individual advantages and limitations, the latter category seems to be a good compromise between a data-driven strategy and object- oriented modeling. It is flexible in the sense that it is not restricted a-priori to specific building shapes. On the other hand, there are still object-oriented aspects included since typical building regularities are to identify in the InSAR data. Concerning the utilization of shadow and layover effects one has to keep in mind that, especially in urban areas, layover appears very often and may also cover shadow from neighboring buildings. Hence, shadow areas are usually hard to

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