THE NEW ORTHORECTIFICATION STRATEGY AS A WAY FOR 
VISUAL QUALITY IMPROVEMENT 
Krystian Pyka 
Department of Photogrammetry, AGH University of Science and Technology, Krakow, Poland - krisfoto@interia.pl 
KEY WORDS: Edge, Orthoimage, Orthorectification, Pixel, Quality, Visualization, 
ABSTRACT: 
In the paper a new orthorectification strategy of aerial images is presented. The strategy is focused on improvement of visual quality 
of orthoimage. The idea of proposed strategy relies on extraction of edges from the image and next special resampling is taken. The 
critical issue for edge detection is that the images usually includes some noise. The de-noising of an image has many solutions but 
none of them is perfect. Therefore in the paper the usefulness of wavelet transformation for image de-noising is tested. 
The proposed orthorectification strategy gives a better image then the image from typical orthorectification. The visual quality of 
edges in orthoimage is comparable to the original image. 
1. INTRODUCTION 
The paper includes a proposal of a new orthorectification 
strategy of images. 
The geometric transformation of images especially the 
transformation between the central perspective image and the 
orthoimage is performed in the same way for many years. The 
only changes that have been implemented, rely on the 
resignation from any simplification in the computing process. 
Now more often the exact transformation using collinearity is 
computed for all pixels and breaklines of the terrain are taken 
into account. These changes are sufficient to achieve the good 
geometric quality, but the visual quality of orthorectified image 
is always worse than the visual quality of the original image. 
The reason why visual quality is reduced is that 
orthorectification is the same both for linear elements and for 
homogenous areas. In the typical rectification the intensity 
value of an orthoimage pixel is a result of interpolation of the 
intensity values of a group of image-pixels (from one to 
sixteen). Therefore on the intensity of pixel on the edges of a 
road is influenced by the intensity of a road surface and 
shoulder. 
The proposed modification of orthorectification strategy is 
focused on the improvement of visual quality of an orthoimage. 
The main steps of the proposed strategy are: (1) image de- 
noising using wavelet transformation, (2) detection of edge 
using Laplace operator, (3) the execution of orthorectification 
with a special resampling method. 
2. IMAGE DE-NOISING USING WAVELET 
TRANSFORMATION 
Aerial and satellite images usually contain noise, due to image 
acquisition, transmission errors or compression side effects. The 
noise causes great problems to image processing algorithms. 
(Pitas, 2000). Only when the image de-noising is effective the 
edge detection is proper. In other case the edge detection is 
significantly noised. 
De-noising is typically handled by smoothing filters. But 
smoothing can delete useful information or distort the input 
image. The mainly used smoothing method is the Gaussian 
2.D filter. The Gaussian outputs are an average of each pixel's 
neighborhood weighted more towards the value of the central 
pixels. In comparison with a mean filter, the Gaussian provides 
a gentler smoothing and preserves the edges better. But the 
Gaussian filter works well only in isotropic spaces therefore 
when it is used for smoothing real images it removes both noise 
and detailed information. 
An other solution to de-noise is the usage of the wavelet 
transformation. This transformation separates the smooth 
variations and details of the image much better than Fourier- 
based techniques. 
Wavelet transformation is widely applied to image compression 
and is more often used for image de-noising (Rangarajan, 
2002). 
Wavelet transformation decomposes a discrete image into four 
sub-image where LL, HL, LH, HH represent average, vertical 
fluctuation, horizontal fluctuation and diagonal fluctuation 
respectively. The decomposition may be held on several levels 
(Walker, 1999). A sample of all the sub-images on the first 
level is shown in the figure 1b. As wavelet transformation the 
Haar function was used. 
The basic method of image de-noising is thresholding. By 
choosing a correct threshold, it is possible to remove most of 
the random noise. In this research a soft thresholding adequate 
for each sub-image was tested: 
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