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Proceedings of the Symposium on Global and Environmental Monitoring

Areas of landcover changes are usually a
little in object images. If a landcover
map of the object area exists before
analyses of a set of multi temporal
images, most of pixels included in each
category on the map should correspond to
correct training area of its category.
Thus roughly correct training area can be
decided by overlaying the the existing
landcover map on a object image in a set
of multi temporal images.
Training data of each category correspond
to all pixels data in its training area
on the object image. The used existing
landcover map and obtained training data
is named as a reference map and initial
training data, respectively.
Since a few number of pixels included in
the initial training data may be
incorrect training data because of land-
cover change between the reference map
and the object image, it is necessary to
remove these pixels from the initial
training data. Those pixels are named as
change pixels. Change pixels can be
detected by searching pixels at a long
distance from a centroid of each category
in feature space. And classification
classes, which change pixels should be
assigned, can be generated by using
clustering methods. Consequently, we may
use the data combined generated classes
by clustering for change pixels and
remaining initial training data after
removing of change pixels as final
training data.
As a result of above discussions, a
following five stages procedure can be
obtained(Fig. 1 ) . At the first stage,
initial training data are extracted by
overlaying a reference map on each object
image. If a reference map can not be
prepared, one object image in a set of
multi temporal image data should be
classified using a conventional method.
And the classified result may be used as
reference map.
At the second stage, change pixels are
extracted from the initial training data.
In this study, Mahalanobis' distance is
used to search change pixels. At the
third stage, clustering is performed for
change pixels and new classification
classes are generated. At the fourth
stage, final training data are construct
ed by combining generated new classes and
remaining of the initial training data.
At the final stage, a object image is
classified using obtained final training
data. Maximum likelihood classifier are
used in this study. Multi temporal land-
cover maps can be obtained by applying
this procedure to each image of a set of
multi temporal images.
In order to evaluate the proposed
automatic classification procedure,
following four temporal Landsat TM data
were classified by using this method and
a conventional method.
[object area]
Sagami River basin(12.8Kmxl2.0km)
[observation date]
Nov.4(1984), Jan.23(1985),
Aug.6(1986), May 21(1987)
[image size]
512x480 pixels, 25mx25m/pixel
1 , 2,3,4,5, and 7
Classification results of conventional
method are used to compare to classifi
cation results of proposed method. And
also, there are used as reference maps.
The reason of preparing four reference
maps is to evaluate dependences of a used
reference map.
Items in left hand side in Table 1 shows
classification categories which were used
in the conventional classification. The
number of categories are fourteen. But
the total number of classification
classes are about sixty since each
category has several cub-classes.
Classification accuracies were estimated
quantitatively by using digital test site
data as shown in Figure 2. Test site data
contain about 60 1 and-cover/use
categories. As the categories used in
land-cover/use test site data differ from
those used in landcover classifications,
fourteen landcover categories were merged
to five major categories as shown in
Table 1. Accuracy evaluations were
performed based on these five major
Change pixels were extracted by searching
pixel which exist at longer than 2.5
Mahalanobis' distance from a centroid of
each categories containing these pixels
in the initial training data. Figure 2
shows obtained change pixels of each
temporal image. Change pixels had a area
about 20%-30% for each temporal image.
About 40 to 60 classes were generated by
clustering for change pixels. Used
clustering method is a hierarchical Ward
method, namely, the number of classifi
cation classes of final training data is
about 100-120 classes.
Table 2 shows estimated classification
accuracies of classification results of
the conventional methods and the proposed
method for each temporal image. Mean
accuracies shown in Table 2 were area
weighted mean values for each category.
The results of conventional method were
derived by using training data which are
extracted and modified by skilled
operator. The skilled operator performed
detailed ground truth before training
area selection and during modification
procedure of extracted training data.