In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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over-determination in the spatial point intersection. 
Stereo matching of a TerraSAR-X image pair is improved by 
including geometric constraints. First, one image is pseudo 
epipolar registered based on an affine polynomial transformation 
using both sensor models. Second, in image matching a starting 
location for each pixel is predicted, again using sensor models 
and a coarse DSM (SRTM or ASTER model). 
The presented approach yields an areal digital surface model. 
When subtracting a reference digital terrain model (DTM), 
e.g. available from airborne laser scanning, a canopy height 
model (CHM) can be extracted (cf. Figure 2 and Eq. (1)). Such 
CHMs serve as an important information for the retrieval 
of forest parameters. As mentioned before, the canopy 
height underestimation can be quantified using laser scanner 
ground truth data. Such comparison enables to determine the 
underestimation factor r in percent. In regions of forest the 
TerraSAR-X DSM is then corrected by multiplication with the 
factor 1/(1 — r/100). The forest segmentation presented in the 
next section is then used to correct the canopy height bias (see 
Figure 1). It should be noted that this problem is not straight 
forward, as such underlying image segmentation often is just not 
available. 
Figure 2: Explanation of relation between digital surface model 
(DSM), digital terrain model (DTM) and canopy height model 
(CHM). 
CHM = DSM - DTM (1) 
2.2 Forest Segmentation 
The proposed forest segmentation should allow separating 
regions of forest from non-forest areas. Recently, first results on 
this topic were published in (Breidenbach et al., 2009). They 
perform the classification on TerraSAR-X backscatter mean and 
standard deviation statistics alone. We extend their method by 
including backscatter intensity and texture information, a 3D 
canopy height model and interferometric coherence information. 
For classification a supervised approach is chosen by selecting 
multiple regions together with their ground truth class labels 
(forest / non-forest) and training a maximum likelihood 
classifier. This classifier is then applied to the whole spatial 
extent of given images. The resulting classification is 
constructed with a GSD of 5 meters. Next, very small areas are 
rejected based on a region labeling approach. 
Texture Description. As observed in (Breidenbach et al., 2009) 
regions of vegetation are less textured, i.e. more homogenous, 
than regions of settlements or agricultural areas. (Haack et al., 
2000) suggest to describe this texture information by a variance 
filter. However, our tests showed that such simple parameter is 
not working satisfactorily on TerraSAR-X data. Therefore, we 
choose the Texture-transform (Tavakoli Targhi et al., 2006) which 
is invariant to illumination, computationally simple and easy to 
parameterize so that it also performs reasonably on high resolu 
tion radar data. This transform can be seen as a spatial frequency 
analysis, where the key idea is to investigate the singular values of 
matrices formed directly from gray values of local image patches 
(the backscatter information in our case). More specifically, the 
gray values of a square patch around a pixel are put into a ma 
trix of the same size as the original patch. The texture descriptor 
is computed as the sum of some singular values of this matrix. 
The largest singular value encodes the average brightness of the 
patch and is thus not useful as a texture description. However, the 
smaller singular values encode high frequency variations charac 
teristics of visual texture. Therefore, the singular values of this 
matrix are sorted in decreasing order. Then the Texture-transform 
at each pixel is defined as the sum of the smallest singular val 
ues. For the tests several window sizes and singular values ranges 
were chosen, where a window of size 33 x 33 and a range of 20 
to 33 smallest singular values performed best. 
Canopy Height Model. Obviously, vegetation heights are a use 
ful information to segment regions of forest. The canopy height 
model is extracted employing the methodology described in Sec 
tion 2.1. 
InSAR Coherence. For forest segmentation the interferometric 
coherence, which is a measure of the interferogram’s quality, 
can be of great value since regions of vegetation suffer 
from temporal decorrelation (see also the detailed study 
on interferometric decorrelation (Zebker and Villasenor, 
1992)). The standard coherence estimation is based on a local 
complex cross-correlation and is known to over-estimate 
the real coherence value. In general, a larger window within 
cross-correlation provides a better coherence estimate. At the 
time of radar sensors like ERS the standard procedure was 
to estimate the coherence over the same window used for 
multi looking. As the multi looking sizes become smaller for 
TerraSAR-X imagery the coherence was highly over-estimated 
resulting in a noisy coherence image. Therefore, a decoupling 
of the window size of multi looking and cross-correlation is 
introduced. The resulting coherence estimate uses a correlation 
window of 10 x 10 pixel and a multi looking window of 2 x 3 
pixel (azimuth x range). Regions of very low coherence 
correspond mainly to vegetation (forests and agricultural areas). 
Thus, such coherence information is used in the classification 
process as one feature. 
3 TEST DATA 
Within the AT-X project the proposed algorithms have been ap 
plied to several test sites. For the presented study only a single 
test site called “Burgau” is chosen to keep the results clearly ar 
ranged. The test site of interest spans an area of 12 x 12 km 2 
in Austria. This rural test area covers agricultural as well as for 
est areas and shows flat to slightly hilly terrain, the ellipsoidal 
heights ranging from 270 to 445 meters above sea level (cf. Fig 
ure 3). The forested regions in the area mainly consists of dense 
stands of deciduous trees. 
Multi-Image DSM Generation. For multi-image DSM 
derivation the test data consist of multiple TerraSAR-X 
multi-look ground range detected (MGD) Spotlight products 
from ascending, respectively descending, orbit. All images were 
ordered as single-polarization products (HH) with science orbit 
accuracy and were acquired in the period of July and August 
2009. Table 1 reports the major parameters of the “Burgau” test 
site. It should be noted that the images acquired at steep look 
angles (i.e. MGD_ascl and MGD_dscl) have a lower GSD than 
all other products.