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Mapping without the sun
Zhang, Jixian

when the window size continues to increase, the classification
accuracy increases but smoothly and slowly until it reaches to
the highest value when the window size increases again;
afterwards, with the increase of the window size, the
classification accuracy decreases.
So we can conclude that for a certain ground object, the
estimated scale can be used as the suboptimal selection for the
processing window size during the texture extraction. Examples
are given in Figure 7.
B.B. Chaudhuri and N. Sarkar, 1995. Texture segmentation
using fractal dimension. IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 17, no.l, pp.72-77.
B.S. Manjunath and R. Chellappa, 1991. A Note on
unsupervised texture segmentation. IEEE Trans. Pattern Anal.
Mach. Intel!, vol. 13, no. 5, pp. 478-483.
Growth Rate of Classification Ac curacy
g,.. -“-(-f'—
1 2
3 4 5 6 7 8 9
10 11 12 13 14
15 16 17
□ Cor.forVeg.
Figure 7. An example of growth rate of classification accuracy
on the change of window size for vegetation
3.3 Multi-scale Texture Feature Classification
According to our experiments, we find that different texture
feature has different presentation for a certain ground object.
Correlation image is suitable for vegetation recognition,
Entropy image is suitable for residential area classification, and
Stand Deviation image has the similar interpretation abilty for
vegetation, residential area, and water body. The optimum
multi-scale features selected can give an improved classification
performance compared with a single-scale classification with
the given result in Table 2. 4
COR 11/ENT 13/SD 21
Single-scale: 11
Class Type
Prod. Acc.
User Acc.
Prod. Acc.
User Acc.
Water Body
Residential Area
Overall Accuracy
Table 2. Classification accuracy for different scale
C. M. Pun and M.C.Lee, 2003. Log-polar wavelet energy
signatures for rotation and scale invariant texture classification.
IEEE Trans. Pattern Anal. Mach. Intel!, vol. 25, no. 5, pp. 590-
D. A.Clausi, 2000. Comparison and fusion of co-occurrence,
Gabor and MRF texture features for classification of SAR sea-
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statistics as a function of grey level quantization. Canad. J.
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probabilities and Markov random fields for texture analysis.
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filter parameters for optimal texture separability. Pattern
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synthetic aperture Radar sea ice imagery using a novel Markov
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ice imagery using grey level co-occurrence matrices. IEEE
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GLCP-based methods appear to be the most commonly used
and are the most predominant method for image texture analysis
and presentation. A multi-scale texture analysis method based
on co-occurrence probabilities for texture analysis of single
band and single polarization SAR imagery is presented and
implemented in this paper. Experimental results prove that it
can generate improved classification results compared with a
single-scale based classification scheme. Furthermore, the semi-
variogram model based method can estimate the scale of ground
objects which can be further used as the suboptimal selection
for the processing window size during the texture feature
R. M. Haralick, K. Shanmugam, I. Dinstein, 1973. Textural
features for image classification. IEEE Trans. Syst. Man Cyber.
vol.3, no.6, pp. 610-621.
S. E. Franklin, 2001. Remote sensing for sustainable forest
management. Boca Raton, FLA: Lewis Publishers.
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