The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
1350 
GCPs were collected by a RTK GPS survey assisted by the 
RESNAP-GPS permanent network of the Lazio Region, 
(managed by the Area di Geodesia e Geomatica - Sapienza 
Università di Roma) with a 3D accuracy of 15 cm. The block 
triangulation was performed both by Leica Photogrammetric 
Suite (LPS) 9.1 and BLUH software. Finally, the image 
matching and the DSM generation at 2m grid spacing were 
performed using LPS. 
Figure 1. Overview of Cartosat-1 Band A. The image size is 
12’000 x 12’000 pixels. Some crater lakes are visible. 
3 RADIOMETRY ANALYSIS 
Within the chain from image sensing to the final value-added 
product the quality of the images plays a crucial role. Image 
quality is defined by several parameters, as the radiometric 
resolution and its accuracy, represented by the noise level, and 
the geometrical resolution and sharpness, described by the 
Modulation Transfer Function (MTF). In the next sections these 
parameters are investigated for the scenes used in this work. 
Other radiometric problems, like vertical striping effects, have 
not been observed at a significant level. 
3.1 Image noise analysis 
The image noise characteristics are important for the image 
matching process in the DSM generation. 
Nowadays, most of the linear array sensors have the ability to 
provide more than 8-bit/pixel digital images. The Cartosat-1 
sensor provides images with 10 bit/pixel, that means 1024 
available grey levels, but 99% of the original pixel values vary 
between 0 and 255. 
A preliminary analysis was carried out to investigate the noise 
dependency on image intensity. According to the method 
proposed in (Baltsavias et al., 2001), and (Zhang, 2005) the 
noise characteristics of the stereopair were analyzed using the 
standard deviations of the grey values in non-homogeneous 
image regions, that allow to evaluate the noise variations as a 
function of intensity. 
In order to evaluate the noise level, a window (3x3 pixels) is 
moved within the area by 3-pixel steps in both directions and 
the standard deviation and the mean grey value are calculated 
for each window. The grey level range is divided in bins, and 
the standard deviations are assigned to a bin according to the 
mean grey value of each window. In each bin the noise is 
estimated as the mean of the 5% smallest standard deviations, 
under the hypothesis that the variability within the window 
should represent just the noise and not the different texture. The 
results reported in Figure 2 indicate that the noise is intensity 
dependent showing smaller noise values for the bins of low 
grey levels than for the high grey ones. This is a well known 
effect, also present in digital aerial images. 
NOISE 
CNTfCDCOOCM’^-lOT-rv.cO 
C0CDO5CMCDO>CNLOt— <OCM 
COCOr^-Ov-COlOCOCNi 
oiCNicooicqmx-co 
T- T- T- CM CM in <o 
BINS 
Figure 2. Cartosat-1 noise level estimation. 
3.2 Modulation Transfer Function (MTF) analysis 
The image sharpness was addressed through the analysis of its 
Modulation Transfer Function (MTF) that represents, in the 
spatial frequency domain and for a given direction, the image 
spatial resolution. The transfer function of the system can be 
obtained considering the response (Edge Spread Function - 
ESF) of the optical system to an ideal edge (rectangular pulse). 
Multiple methods have been proposed for determining the MTF 
of remote sensing systems in-orbit. Most of the procedures use 
specific artificial or natural targets on the ground for estimating 
the Edge Spread Function (ESF). Through the first derivative of 
the ESF it is possible to obtain the Line Spread Function (LSF), 
whose Fourier Transform provides the Modulation Transfer 
Function (MTF) (Figure 3). 
Bright Side 
Figure 3. Edge MTF estimation method.