# Full text: Mapping without the sun

```correlation
coefficient
c 'c=ZZl/('j)-/v fcoj)-/',]/
y-0 /-0 / 1
M-l N-l _ . M-lN-l. ,
YLVV'fi-PrìYLWfi-rì
7=0 i=0 7=0 i=0
PSNR
PSNR = -101og 10 — MSE = f-t i f j \f(i,j)-g(i,j)Y
¿55 MN j —Q l= o
Table 2 Definition of image similarity, image fidelity,
correlation coefficient, and PSNR
3.1.3 Image application analysis
Image application analysis refers to study the effects of lossy
compression on image quality from the aspect of image
application. The common image applications are image
classification, image target recognition, image character
extraction and spatial pattern analysis.
3.2 Geometric quality assessment
The geometric quality of remote sensing image reflects the
reconstructed image in keeping geometric characters, such as
feature’s shape, size, etc. The goodness of geometric quality
determines the degree of image measurability. In many high
precision image processing application, such as digital
photogrammetry, computer graphics, it is very important to
study the effects of compression on geometric quality.
In surveying and mapping area, several methods, like image
matching accuracy analysis, automated DSM extraction
accuracy analysis, photogrammetric point determination
accuracy analysis, etc, can be used to assess the effects of
compression on geometric quality.
3.2.1 Image matching accuracy analysis
Image matching accuracy analysis shows how degradation in
image quality associated with lossy compression can affect
matching accuracy.
Let Ax and Ay refer to image matching error, then:
Ax = x / -x g > Ay = y f -y g (7)
(xf,yf) is the pixel value of original image, and (x g , ) is
the corresponding pixel value of reconstructed image. The
matching algorithm - least square matching is a good choice
since it can reach the accuracy of 1/10-1/100 pixel.
3.2.2 DSM extraction accuracy analysis
As to stereopairs, Automated DSM extraction accuracy analysis
and photogrammetric point determination accuracy analysis are
necessary [Tian-Yuan Shih et al, 2005; Zhilin Li et al, 2002].
A DSM is extracted from the original stereopair. This DSM is
held as the reference against which the terrain heights obtained
from compressed imagery are compared. The DSMs can also be
derived for each of the compressed pairs, with the same exterior
orientation parameters. DSM extraction accuracy analysis is
conducted through comparing two DSM data-sets with X, Y, Z
format that are generated from original images and compressed
images, respectively. The relative error in elevation can be
measured at the identical points by comparing the
uncompressed and compressed images with root mean square
error (rmse) [Tian-Yuan Shih et al, 2005].
3.2.3 photogrammetric point determination accuracy
analysis
Similar to DSM extraction accuracy analysis, photogrammetric
point determination accuracy analysis is to compare the
accuracy of two sets of 3D coordinates of the feature points that
are from original images and reconstructed images [Zhilin Li et
al, 2002].
4. A PROTOTYPE FOR IMAGE COMPRESSION
QUALITY ASSESSMENT SOFTWARE
Based on the above research, we have designed and
implemented a prototype for image compression quality
assessment software. The major functions include image
character analysis, image comparison analysis, and image
matching accuracy analysis, etc. Figure 1 shows the interface.
(a)
Figure 1 Image Compression Quality Assessment Software
Interface
5. CONCLUSION
Quality assessment for remote sensing image compression is
critical to compression algorithm designers and image products
users. Through the above research, a series of experiments were
conducted. As to the compression ratio adopted in Resources
Satellite-3 (a high resolution stereo mapping satellite),
compression ratio should be no more than 4:1 if JPEG2000 or
SPIHT algorithm were employed on board.
REFERENCES
Ahmet M. Eskicioglu, 2000. Quality Measurement for
Monochrome Compressed Images in the Past 25 Years. IEEE
International Conference on Acoustics, Speech, and Signal
Processing, Istanbul, Turkey, pp. 1907-1910
Ferwerda, J.A., Pellacini, F., 2003. Functional difference
predictors (FDPs): measuring meaningful image differences.
Conference Record of the Thirty-Seventh Asilomar Conference
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