jidable difference in 
lumination or even 
1c differences in the 
ngly, in the corres- 
compensated as part 
ghtness and contrast 
with the heights re- 
ymetric observation 
N(CDN+b) (4) 
own parameters: the 
quired contrast and 
additional equations 
on the Xoffset and 
ned by equation (3). 
oth (3) and (4); the 
‘) impact on the re- 
d Intensity is based 
operly, the relation 
> and after the ad- 
ntensity observation 
hts for the intensity 
oretically, needs to 
tation. However, it 
g with the overall 
to level largely dif- 
ding on terrain and 
ors between 1 and 
regions where the 
exclusively deter- 
Analysis input, the 
etric matching are 
of all, the roles of 
ween reference and 
ont offset computa- 
The average offset 
indicators are mea- 
of point/plane pairs 
ons as well as the 
| limits can depend 
errain and imaging 
t general settings — 
S, based on at least 
ber of input image 
rrect results; but as 
d in a fairly dense 
he Shear Analysis. 
sitives. 
DATA SETS 
cloud matching ap- 
data — heights and 
; — and artificially 
id Shear Analysis 
f ADS blocks with 
for the replacement 
the verification of 
automatically derived offsets against those human measure- 
ments. The comparative analysis of the geometric and the com- 
bined matching approaches is documented below. 
3.1 Data Sets 
The ADS test data used here have been captured and processed 
by North West’s production. In the context of Shear Analysis 
verification, strip offsets were automatically derived from a 
very dense, practically continuous pattern of info cloud patches 
of 512 x 512 image pixels in size (Table 1 and Figure 2); a re- 
presentative number of manual QC measurements is available. 
3.1.1 Georgian Bay: Located on the coast of Lake Huron's 
Georgian Bay in Ontario, Canada, this block is dominated by 
forest. It is in parts dense but generally includes clearings and 
aisles, and features different tree species of various heights. The 
imagery has been captured in 2009 for the Ontario Ministry of 
Natural Resources (OMNR) for forest inventory; it is a typical 
forest data set. 
3.1.2 Lansing: This block, captured in fall 2011, shows the 
City of Lansing, Michigan, approximately in its center. Accor- 
dingly, the data contains predominantly urban and suburban 
areas as well as some forest, fields and smaller lakes. 
This block is analyzed in more detail by Gehrke et al. (2012), 
comparing different georeferencing and also demonstrating the 
possibilities of Shear Analysis. See also section 5. 
3.1.3 New Mexico: This 2011 data set is part of the National 
Agriculture Imagery Program (NAIP). From a very large ADS 
block in South-Eastern New Mexico, a single strip overlap was 
selected for this investigation. It features mountains and flat 
desert areas with only little vegetation (Figure 2). 
  
  
  
  
  
  
  
  
Data Set CS Terrain Strips | Patches 
Georgian Bay 0.30 | Forest, Water 4 113 
Lansing 0.30 | (Sub-)Urban 14 808 
New Mexico 1.00 | Mountains 2 378 
  
Table 1. ADS data sets used for verification of the point cloud 
matching. 
3.2 Accuracy in Comparison with Manual Measurements 
One way of verifying the automatically derived ADS strip off- 
sets is their comparison with manual QC measurements, which 
are available for all ADS blocks in North West production. This 
comparison was carried out for all patch locations that feature 
corresponding measurements, provided that successful and 
reliable offsets from both solely geometric and combined geo- 
metric/radiometric point cloud matching exist. (See below for 
the success rates of both methods.) Resulting averages and 
standard deviations of the X, Y and Z differences between 
manually measured and automatically derived strip offsets are 
shown in Table 2. Note that, even though offset locations on 
ground are practically identical, the orientation parameters used 
in their computations differ: Human measurements are naturally 
based on a stereo pair — ADS backward and forward bands in 
this case —, but the SGM for the automatic method utilizes all 
three ADS views to increase redundancy. Especially after aerial 
triangulation, the impact of remaining orientation errors is ex- 
pected to be very small but, nevertheless, can act systematically 
for this comparison. 
105 
  
Combined Matching 
Data / 
Axis Average Std. Dev. Average Std. Dev. 
[GSD] [GSD] [GSD] [GSD] 
Geometric Matching 
  
  
Georgian Bay, 23 Points/Patches 
  
X 0:37 + 0.09 0.43 0.34 + 0.08 0.36 
Y 0.37 + 0.08 0.38 0.14 + 0.08 0.37 
Z 0.07 3: 0.12 0.59 0.06 +£0.12 0.59 
  
Lansing, 33 Points/Patches 
  
0.01 £0.08 0.46 
0.05 + 0.04 0.23 
0.05 + 0.06 0:32 
X 0.17 + 0.09 0.53 
Y 0.05 + 0.05 0.30 
Z 0.05 + 0.06 0.33 
New Mexico, 28 Points/Patches 
X 0.43 + 0.16 0.85 
Y 0.22 + 0.08 0.42 
  
  
0.29 + 0.09 0.45 
0.20 + 0.06 0.34 
  
  
  
  
  
  
Z 0.30 + 0.07 0.39 0.30 + 0.07 0.39 
  
Table 2. Averages and standard deviations of differences be- 
tween manually measured and automatically computed ADS 
strip offsets. 
Looking at the offset differences and standard deviations in 
Table 2, it can be seen that the combined geometric/radiometric 
point cloud matching agrees better with manual measurements 
than the geometric matching. As expected, the consideration of 
intensity improves planimetric offset components for all data 
sets. The majority of the average differences is not significant; 
however, the highest significance level occurs in the forest data 
set for geometric matching. This can be assigned, at least partly, 
to the limitations of the approach, but the above-mentioned ori- 
entation differences might add to that. The largest planimetric 
discrepancies tend to occur across flight direction. This corres- 
ponds with the general observation of North West’s production 
that, after aerial triangulation, remaining orientation inaccura- 
cies and, therefore, local strip offsets are largest in this direction 
(but well within customer specifications); see also Gehrke et al. 
(2012): 
  
  
  
  
  
  
  
Manual | Geometric | Combined 
Data Set / Axis Results Matching | Matching 
[GSD] [GSD] [GSD] 
Georgian Bay 
X Flight Dir. 0.26 0.34 0.18 
Y 0.48 0.44 0.33 
Z 0.53 0.22 0.22 
Lansing 
X 0.45 0.40 
Y Flight Dir. n/a 0.26 0.13 
Z 0.15 0.15 
New Mexico 
X 0.29 0.72 0.29 
Y Flight Dir. 0.32 0.34 0.26 
Z 0.54 0.33 0.33 
  
  
  
  
  
  
Table 3. Offset standard deviations throughout strip overlaps, 
for Lansing and Georgian Bay RMS values based on all over- 
laps. The number of manual measurements per overlap in Lan- 
sing varies between 3 and 5, which is not representative for the 
derivation of reliable statistics.