International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
OINT ES Scanning Sensor- Sensor- Numbers BER accuracy GCP measurement 
Image Date of acquisition Azimuth | Elevation (m) 
F mode of GCPs method 
(deg) (deg) 
Geneva Q 2003-07-29 Reverse | 2864 77.6 67 93-05 Sig Faser 
Geneva | West 2001-05-28 Forward | 253.6 67.2 34 0.303 Orholmage Moser 
iria, sa DTM 
Geneva I East 2001-05-28 Reverse 240.2 61.6 44 03:05 ps f loser 
Thun I 49 000 2003-12-11 Reverse | 140.35 62.78 25 0.2-0.3 GPS 
Thun I 49 100 2003-12-11 Reverse 66.41 63.56 25 0.2-0.3 GPS 
Fm | Thun I 51 000 2003-12-25 Reverse | 180.39 62.95 24 0.2-0.3 GPS 
rmoimage, Thun I 51 100 2003-12-25 Reverse | 72.206 82.13 24 0.2-0.3 GPS 
Thun I 54 000 2003-12-25 Forward | 128.17 82.62 24 0.2-0.3 GPS 
  
Table 1. Specifications of used satellite images and respective GCPs (Q stands for QUICKBIRD and I for IKONOS). 
' analyzing 
irst dataset 
econd area 
%. In both 
sed in both 
ble number 
ompared to 
articular so 
Im or less. 
he images. 
r DTM. A 
“occlusions 
iile in open 
S, and to a 
IOS images 
> QB image 
OS images. 
et (western 
) km) were 
1e day (see 
lap, and the 
2a by snow, 
nage in the 
2. IMAGE ANALYSIS 
2.1 Radiometric Quality 
HRS usually employ TDI technology. All IKONOS and QB 
images have been acquired using 13 stages of the TDI. A higher 
number of stages would increase the signal but also the danger 
of saturation, especially for bright objects. TDI results in 
smoothing and a reduction of the MTF. MTFC is always 
applied by SI and although in the QB metadata nothing is 
mentioned, it is fairly probable that a similar process is applied. 
DRA is optional with IKONOS, but with QB although again 
nothing is mentioned in the metadata, it seems that it is applied 
by default (this is indicated by the respective histograms which 
show saturation in the maximum grey value of 2047). The 
histograms of both IKONOS and QB show that only 8-9 bit are 
essentially used, while the blue channel has the smallest range 
of grey values. 
The noise characteristics of the images were analysed and 
quantified using the standard deviation of the gray values in 
homogeneous (Lake of Geneva, Lake of Thun) and 
inhomogeneous areas (large image parts without homogeneous 
areas). The use of homogeneous areas is justified as noise is 
especially visible in such areas, whereas the use of 
inhomogeneous areas allows an analysis of the noise variation 
as a function of intensity and when homogeneous areas are 
missing. Baltsavias et al. (2001), in their first assessment of 
PAN channels, whereby the values for IKONOS are average 
values. Table 3 indicates that noise is intensity dependent for all 
images, however for QB the noise increases less with intensity. 
When the number of samples in a bin is less than 50, no value is 
given. The lower noise of QB may be due to a better 
preprocessing of the QB images, or due to the imaging 
conditions (e.g. higher elevation), or due to the fact that QB 
while scanning the scene, e.g. from North to South continuously 
rotates from South to North in order to achieve the nominal 
pixel size for PAN, thus oversampling. But it can also be 
accidental, or due to uncertainties in noise estimation in 
inhomogeneous areas. Thus, more tests with QB images 
involving also homogeneous areas are needed. For the MS 
channels, in both IKONOS and QB, the noise pattern is similar 
to PAN, however due to the lower dynamic range (shorter 
integration time), less bins have a significant number of 
samples. 
  
Ikonos Red | Green Blue NIR PAN 
images 
  
Geneva East 1.89 2.34 2.35 2.02 5.05 
  
Stereo A | 1.54 1.98 2.21 1.73 4.20 
Thun 
  
  
Stereo B Thun | 1.61 177 1.93 1.66 4.13 
  
  
  
  
  
  
  
Table 2. Noise estimation for homogeneous areas in 
IKONOS images. 
  
  
  
  
  
EI n IKONOS Geo, give a short description of the method utilised PAN 0 — | 128- | 256- | 384- | 512- | 640- | 768 — 
Ny find 4m for noise estimation. The method has been modified regarding posses 127 22 = > em 2% s 
Was used, noise estimation in inhomogeneous areas, in order to adapt Can = n m E FIG WE. 
à PAN and computation of the standard deviation according to the number SIE Q E ug US AER e 
| RPC files. of significant samples in each bin (grey level range). LUE 2 7 7 7 
used inthe yn aon existed only in the IKONOS Foe image er ge Ta 1238 155 1300 139 1735 + 
size and ca. eneva and the eastern Thun stereo pair. In QB, due to wind, tet 
with 0.5 m 
images with 
| 25m DTM 
of the GCPs 
n all cases, 
ically using 
ugh lines or 
2-04 min 
SM with an 
id 1.5 m for 
r the DSM 
the water surface was not homogeneous and could not be used. 
The mean standard deviation is computed out of the N% (here 
85%) smallest percentage of samples. According to Table 2, the 
noise in the Thun images is slightly less than in Geneva and the 
MS exhibit less noise than the PAN ones, possibly due to the 4 
times larger pixel size. Considering the fact that the 11-bit data 
represent actually only 8-9 bit, the noise is quite high for PAN, 
a fact that could be verified visually by strong image contrast 
enhancement. 
Estimation of noise in inhomogeneous areas uses as input a 
range of standard deviations in each bin, based on which a 
percentage is computed. The standard deviation in 
homogeneous areas is used to compute the input range. For the 
Geneva IKONOS images, the values of the input range were set 
to 3.5 for PAN and 1.5 for MS. For the QB PAN, the range has 
been empirically set to 1.7. Table 3 shows the results for the 
523 
  
  
  
  
  
  
  
  
  
Table 3. Noise estimation for inhomogeneous areas and different 
grey value ranges (bins) in PAN images.