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Mapping without the sun
Zhang, Jixian

extraction and the classification, first must analyze the
information characteristic earnestly.The grey level curve of
different ground features on different wave bands is as
following figure 2.
Figure 2. The grey level curve of different ground features on
different wave bands
The texture also is the remote sensing image important
information, which not only has reflected the image grey level
statistics information, but also has reflected the ground feature
itself structure characteristic and the spatial arrangement
relations, so it also is one of the visual interpretation important
symbols[8]. Many research indicated that, it can enhance the
interpretation precision to overlay the texture information on
the primary image spectrum information. In the remote sensing
image, the texture mostly is random, which obediences
statistical distribution, it often described by Grey level Co-
occurrent Matrix. Grey level Co-occurrent Matrix is a matrix
which is constituted by level-two union conditional probability
density between the image grey levels, it has reflected the
correlation between two random points grey level in the image.
In this foundation, we had defined eight texture characteristic
statistical value: Mean, Variance, Homogeneity, Contrast,
Dissimilarity, Entropy, Second Moment,all the value described
the image texture characteristic from many aspects. 1[9J Using
Grey level Co-occurrent Matrix to carry on the texture analysis
involves three Important parameters: motion window size,
motion step and motion direction. The appropriate window size
is especially important to the texture analysis.In general, the
window size is decided by the primary image texture structure,
the small window denotes the slight texture characteristic while
the big denotes the rough. The choice of the motion step is also
decided by the image texture granularity, the short step is
suitable for the slight texture while the long is suitable for the
rough.Many researches pointed out taking the step as one is
quite effective to all the different texture. In studies,we usually
take four main direction values of 0 ,45 ,90 ,135
direction or the mean of these four direction values. 11101 However,
Franklin and the Pebble pointed out the texture characteristic of
single direction was better than the mean of these four
directions. [11] Through analyzing and comparing different
window sizes and steps, the study selected the window whose
size was 5x5 and the step was one.
In addition, because of the region natural condition control and
the human factor intervention, the spatial distribution of the
ground features often have some region differentiation
rules.Therefore, this study also took the geographic coordinates
and the terrain factor as the forecast variables.
In summary, the study used 18 following testing variables: two
geographic coordinates (X, Y), six wave band grey levies of
TM image(l,2,3,4,5,7 bands)(the 6th wave band exception),
seven texture characteristics (Mean, Variance, Homogeneity,
Contrast, Dissimilarity, Entropy, Second Moment (SM)and
three terrain factors (DEM, Slope, Aspect) . Among these, the
slope was separate variable whose value is 0,1,...,7, and the
other were continual variables.The goal variablesof this study
was: Water body, Paddy field, Arid land, Building area,
Road,Vegetation and Subsidenceland.
4.3 Classifying based on CART
In the study ,we first chose 3,200 sample collections for these
above-mentioned testing variables and target variables, then
make use of CART to analyze and study the samples,finally,we
structured a decision tree who had 38 leave nodes. Its studying
accuracy is 92.8% and verification accuracy is 90.3%. The
structure of the decision tree can be expressed into the If-
Then[CF] form very conveniently. The testing route from the
tree's father node to every leaf node corresponded to a rule , so
there were 38 rules in all. For examples:
If (19.688< Mean<=19.958 && 10.000 13 If (42.333 42.000 = 7) CF=0.854
In the If-Then[CF] form, CF indicates the confidence measure
of the rule,and its value region of CF is [0 , 1]. If the value is 0,
then the possibility of the present pixel belongs to the given
class out is also zero; while the value is 1, then the confidence
value is invariable. Making use of the above-mentioned rules
and the following simple matching strategies, we gained the
classified image such as figure 3(a).
1. When only satisfied some one rule, then took the
output category of the rule as the classified category.
2. When simultaneously satisfied multi-rules, then took
the output category of the rule whose confidence
value was more as the classified category.
3. When didn’t satisfied all the rules, then it was not
In order to compare with other classification methods, the study
also had used supervised classification to classify and test, the
classified image such as figure 3(b).
Water body
Paddy field
Arid land
Building area
Figure 3(a). The output image classified by CART