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their shadows in the study site. In the resulting
classification map we have difficulty in recognizing rivers
and a park.
In Figure 10 we illustrate the same classification
result of the ground albedo data set on Sept.2, 1980
classified according to the class statistics obtained from
the ground albedo data set on May 23,1979. Although the
accuracy of classified result is not so good as compared
with that in Figure 8, it is much improved when compared
with that in Figure 9 and we can recognize the rivers and a
park easily in Figure 10.
In Figure 11 we illustrate the same classification
result of the original CCT level data set on Sept. 2, 1980
classified according to the class statistics obtained from
the original CCT level data set on May 23,1979. As is seen
in Figure 11, the accuracy is very poor and the most of
urban, residential area is misclassified. This suggest that
the temporal data analysis can not be done in the form of
original CCT level, as we claimed in the preceeding
paragraphs. In addition to this ,the above examples indicate
clearly a possibility of signature extension between among
data sets taken at different times if those data sets were
converted in absolute albedo uint.
In the next section we describe a processing system
implementing the signature extension of the remote sensing
data.
STRUCTURE OF SIGNATURE EXTENSION SYSTEM
We show the overall data flow in signature extension system
in Figure 12. There are two phases, i.e., the data base :
phase and the table look-up phase in a signature extension :
system. The data base phase consists of three program steps, -
that is, the AECS step, the classification step produces ,
both classification map and the statistical quantities for
certain classes if necessary. The accumlated statistics on
pattern classes are stored in the form of Table of
Reflectance Pattern Class (TRPC) in computer disk. In the
TRPC each class is expressed in terms of three digits. The
first digit gives a basic land cover such as the forest,
sea, urban, and so on. The next two digits specify more
detailed category such as evergreen forest, deciduous forest
and so on. The correspondence between this three digits
class and the actual land cover should be done through the
ground truth. The TRPC should contain the following
information: three digits class number, its mean albedo
values in bands 4,5,6 and 7, its covariance matrix elements,
its scene ID number, the date of exposure and the
reliability bound. The reliability bound gives the seasonal
reliability of class statistics. 0 j
The table look-up phase consists of three steps, other
than the AECS step. In the extraction step an appropriate
part of the large data base TRPC is extracted according to
the date when the Landsat data set to be classified was
taken. If all patterns are normally distributed, it is said
that the Mahalanobis distance D 2 (i) between a point
belonging to a class with class number i and its class
center has a chi-square distribution. In the table look-up
and link-index step we construct confidence regions for