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The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Chen, Jun

ISPRS, Vol.34, Part 2W2, '‘Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001
4(a) Original SPOT Stained by Noise
The figure 3(a) is a SPOT panchromatic image acquired from
Wuhan. There are obvious objects such as roads and some fields
in the image. The figure 4(a) results from figure 3(a) stained by
random noise. The figure 3(b), 3(c) and 3(d) are the results which
are respectively processed by the classic Sobel, Robert
operators and the a trous wavelet decomposition method. In the
condition of noise, we use the methods and get the results shown
in figure 4(b), 4(c) and 4(d). Comparing with the figure 3, we can
find the main edges of the figure 3(d), for example, the two roads
are finer than those in the figure 3(b) and 3(c). The tiny field
edges are obvious in the figure 3(d), but they do not exist in the
figure 3(b) and figure 3(c). From the results of figure 3, the edges
detected by the proposed method are finer and it can detect the
tiny edges.
From the figure 4(b) and 4(c), we find the random noise effect is
apparent, but almost no the noise effect in the figure 4(d).
Therefore the classic Soble and Robert methods can not filter
noise, they are sensitive to noise. The & trous wavelet
decomposition method can detect edges and filter random noise.
The major reason may be that the big features of the image
almost don’t verify at the different scales of wavelet transform, but
the random noise rapidly attenuates with the increasing scales;
on the other hand, the random noise is weakened by adding
some wavelet planes. Therefore, selecting proper scales, we can
detect the edges and overcome random noise. In some
applications we have used the results detected by the £ trous
wavelet decomposition method and the results are satisfactory.
3(b) Sobel Operator
3(c) Robert Operator
3(d) A trouse wavelet decomposition
Fig3 SPOT Image and Detected results
3(a) Original SPOT Image