are treated favorably in the maximum
percentage method as shown in Fig. 1.
(3) Minimum Distance Method
In this method, first, TCA is overlaid
with the target image and the centroid of
ecah category is calculated. Secondly,
the distance between each category and
each cluster is calculated. Then the
category having the minimum distance is
assigned to that cluster. For the dis-
tance, Euclidian distance was used in
this experiment.
In the case of the maximum number method
and the maximum percentage method, the
result is dependent upon the size and
location of training area. In the minimum
distance method, it needs less number of
pixels for TCA compared to other 3 meth-
ods but needs caution on spectral featute
of ICA.
(4) Element Ratio Matching Method
In this method, based on the idea that
the category can be represented by the
Set of clusters, the relation of catego-
ries and cluster is determined by the
ratio of clusters which compose a catego-
ry. For defining the element ratio, area
is necessary. Therefore, the process is
performed by local region as follows.
At first, TCA is overlaid with the result
of clustering, which is classified by the
clusters, and the element ratio of each
category is calculated. Secondly, for
each local region that. is fixed on the
result of clustering, element ratio is
calculated and matched with the element
ratio of each category precalculated.
Then the category having the most similar
element ratio of concerned region is
assigned to that region.
4. EXPERIMENTS AND RESULT
4.1 FLOW OF EXPERIMENTS
In order to evaluate the proposed methods
described in chapter 3, following LAND-
SAT TM data was used in the experiment.
Sagami River basin was seleted ‘for target
area to pereform the quantitative evalua-
tion. This area includes the test site
area which landcover is already investi-
gated and categorized in 52 categories.
sensor : LANDSAT TM
date * Nov. 4, 1984
path-row ^: 107-35
area : Sagami River basin in Japan
pixel size: 25m X 25m
image size: 512 X 480 pixels
At first, clusters were generated by a
hierarchical clustering using Ward meth-
od. Since the image data in remote sens-
ins is very large, usually clustering is
performed with sampled data. In this
experiment, 2500 samples(about 1% of
entire image data) were used to generate
66 clusters. Based on the 66 clusters,
the target image data was classified by a
maximum likelihood method. Secondly,
140
representative area of each category in
the target image(training category area)
was selected. 14 categories were selected
as shown in Table 1. Finally, the rela-
tions of clusters with categories were
determined by 4 methods described in
chapter 3.
To evaluate quantitatively, classifica-
tion accuracy was estimated based on the
test site area. The classification accu-
racy was calculated over 5 major catego-
ries as shown in table | to adjust the
selected 14 categories in target image to
52 test site categories.
4.2 Results of Experiments
(1) Maximum Number Method
Fig. 2 shows the result by this method,
and Table 2 shows the classification
accuracy estimated with the test site
data. As expected, this result shows the
tendency that clusters were labeled with
the categories having large area such as
urban.
(2) Maximum Percentage Method
Fig. 3 shows the result by this method,
and Table 2 shows the classification
accuracy estimated with the test site
data. As expected, this result shows the
tendency that clusters were labeled with
the categories having small area such as
other.
(3) Minimum Distance Method
Fig. 4 shows the result by this method,
and Table 2 shows the classification
accuracy estimated with the test site
area. This result is very similar to the
result(Fig. 2) by a human operator. In
other words, a good result was obtained
with roughly selected TCA.
(4) Element Ratio Matching Method
Fig. 5 shows the result by this method,
and Table 2 shows the classification
accuracy estimated with the test site
area. In this method, 5 X 5 sized local
region was used.
(5) Conventional Supervised Method
For the purpose of comparison, conven-
tional supervised method was executed. In
this method, clusters were categorized by
a human operator. 66 clusters were la-
beled with 14 categories (Table 1) as
Shown in Fig. 6. Table 2 shows the clas-
sification accuracy by this method with
the test site area.
5. CONCLUSIONS
Four categorization methods for clusters
were evaluated by experiments using LAND-
SAT TM data. From the results of experi-
ments, following conclusions were ob-
tained.
(1) In the case of the maximum number
method, the clusters are hardly labeled
with
con
per
harc
rie:
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