Full text: CMRT09

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
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. 
2.1. 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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