In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
Figure 3: Overview of the test site “Burgau”. This topographic 
map shows regions of forest in green color. 
Name 
Look angle 
GSD 
Date 
MGD_ascl 
22.3° 
1.25m 
2009-07-28 
MGD_asc2 
37.2° 
0.75m 
2009-08-02 
MGD_asc3 
48.5° 
0.75m 
2009-08-07 
MGD.dscl 
21.3° 
1.25m 
2009-07-30 
MGD_dsc2 
36.5° 
0.75m 
2009-08-05 
MGD_dsc3 
48.0° 
0.75m 
2009-07-31 
Table 1 : Detailed parameters for the Spotlight images. 
Forest Segmentation. The InSAR coherence used in the 
segmentation process is derived from a TerraSAR-X single look 
complex (SSC) InSAR pair (see Table 2). These images were 
ordered as dual-polarization products (HH,VV) with science 
orbit accuracy and were acquired in March 2008. 
Name 
Look angle 
Date 
SSC-dscl 
36.0° 
2008-03-17 
SSC_dsc2 
36.0° 
2008-03-28 
Table 2: Detailed parameters for the Spotlight InSAR pair. 
Reference Data. To enable quantitative evaluations LiDAR data 
is used as ground truth information. The LiDAR reference 
data covers four measurements per square meter, which are 
processed to highly accurate DSMs and DTMs. While the DSMs 
are automatically extracted, the DTMs are semi-automatically 
generated by classifying regions of vegetation, building, bridges 
and other man-made structures. 
To evaluate the DSMs derived using TerraSAR-X multi-image 
radargrammetry, 30 regions on bare ground and 70 regions in 
forest are selected. The average residual height error over such 
regions describes the DSM quality. Over forest, the average 
canopy height underestimation r is extracted and used to correct 
the canopy height. 
To evaluate the image segmentation quality a ground truth 
reference mask is derived using LiDAR data. The lm GSD 
LiDAR CHM is filtered with an order-statistic filter of size 7x7 
and order 37, i.e. the 75^ percentile. The CHM is then down 
sampled to a GSD of 5m using a 5 x 5 average resampling. 
Next, pixels with a height larger than 8 meters are considered as 
forest regions and small regions are filled to eliminate noise. 
4 RESULTS AND DISCUSSION 
Multi-Image DSM Generation. For visual interpretation some 
detailed results are shown in Figure 4. All results are given with 
a GSD of 2 meters in UTM33 projection. Figure 4(a) shows 
the TerraSAR-X DSM, (b) the LiDAR reference DTM, (c) the 
TerraSAR-X and LiDAR based CHM, (d) the pure LiDAR CHM, 
(e) the TerraSAR-X based height error (i.e. the ground truth 
LiDAR DSM subtracted from the TerraSAR-X derived DSM) 
and (d) a topographic map. The TerraSAR-X CHM corresponds 
visually very well to the LiDAR CHM and to the topographic 
map, however the TerraSAR-X DSM is too low over forest, as 
seen in Figure 4(e). Regions of bluish color indicate height un 
derestimations and such regions are placed in forests or on for 
est borders. The quantitative accuracy analysis is listed in Ta 
ble 3. Heights over bare ground are reconstructed with very high 
accuracy (mean value below 20 cm and standard deviation of 
about 2 meters). The canopy height however is systematically 
underestimated by approximately 27.5% for this test site. In our 
previous work we estimated an average underestimation using 
Spotlight and Stripmap imagery and multiple scenes which was 
26.6%±1.4% (Perko et al., 2010). When correcting the height 
bias with this learned value the reconstruction over forest be 
comes a lot better. In particular it decreases to a residual height 
error of 20 cm, like on bare ground (see Table 3 bottom). Figure 5 
and Figure 6 show detailed analyses of the canopy height under 
estimation w.r.t. the canopy height before and after the discussed 
correction, clarifying that the underestimation over forest can be 
corrected for this test site. 
bare ground 
forest 
ß [m] 
a [m] 
/i [m] 
cr [m] 
r [%] 
asc 
0.07 
2.04 
-5.81 
1.87 
28.5 
dsc 
0.18 
1.90 
-5.45 
1.83 
26.4 
forest after height correction 
asc 
0.22 
1.87 
-1.16 
dsc 
0.08 
1.83 
-0.42 
Table 3: Detailed 3D height analysis of the TerraSAR-X derived 
DSMs. 
Forest Segmentation. The features used for forest segmenta 
tion are shown in Figure 7. Obviously, the most important in 
formation for the segmentation are the InSAR coherence and the 
canopy height model. The confusion matrix in Table 4 reveals 
that 90% of pixels (here one pixel has a GSD of 5 meters) are 
correctly classified. About 8% of non-forest regions are classi 
fied incorrectly as forest. This especially happens in small forest 
clearances which are not seen due to the slant range SAR geome 
try or which result from image matching failures. The 2% of pix 
els classified wrongly as non-forest are mainly small forest stands 
where image matching is unsuccessful and thus such regions get 
interpolated. In addition it should be noted that this evaluation is 
relative to the LiDAR ground truth segmentation. Therefore, the 
achieved accuracy is most likely above the 90% since some arti 
facts exist in the LiDAR model. For instance some power supply 
lines are classified as forest. In comparison to the state-of-the-art 
classification of TerraSAR-X data in (Breidenbach et al., 2009) 
the proposed method performs very well. On first glance their 
method also reaches a classification accuracy of 90%. However, 
the evaluation is based on image blocks with 20 x 20 m 2 . When 
reducing the GSD to 5 meters, like in our study, the classification 
accuracy of (Breidenbach et al., 2009) drops to 72.5%. Obvi 
ously, our method performs better as a diversity of information 
like canopy height model, coherence or texture descriptors is in 
corporated into the classification process.