364 
(6), 
X = 127 ( 1 
R • ) / ( R - R ■ ) 
min v max min 
Where I is the mean theoretical intensity , R m g X and R m £ n is 
a gain and offset factor, respectively. These factors are 
known quantities , namely, R ma x = 2*63 and R m in = ^*^^ for band 
4 of Landsat II. 
We apply these curves in Figure 3 to the band 4 data 
covering the Kanazawa area taken on May 23, 1979 by the 
Landsat II. The solar elevation was 58°at the time of 
observation, we need also to have information on ground 
albedos for a few uniformly extended areas. For such test 
areas, we choose the sea and coastal sand dune in the image. 
The observed average CCT levels and the measured mean albedo 
values for these test areas are tabulated in Table 1 at the 
wavelength X =0.55>um. We may find a curve with an _ 
appropriate value of t which passes throuth the points (X,A) 
having both the observed CCT level and the measured albedo 
value of test areas. It is found from Figure 3 that a curve 
with t =0.4 passes through_such two points, more 
specifically, a point of ( x S ea = ^* 2i *» A sea : ^’^ ) and that 
°f ^ X sand = ^”' - ^’ A sand = P* 1, ( / * Similarly, we can estimate 
the value of tfor other band by making another curves of X 
and A. 
Once the value of t at the time of observation is 
obtained,then the AECS can give a ground albedo data set. 
In Figure 4 we show the 11 class-classification map of the 
Kanazawa area ( 256 x 256 pixels ) computed from the 
original CCT level data set taken by the Landsat II on May 
23, 1979. We also show the same classification map in Figure 
5 , except that the ground albedo data set made from the May 
23,1979 Landsat II data set is used in the computation. In 
both cases the cassification is performed by a 
maximumlikelihood dicision rule and a supervised method 
choosing the same test sites in four band space. We can 
recognize that the quality of the image based on the ground 
albedo data set is much improved, compared with that in 
Figure 4. For example, the river systems,water way 
connecting the port to the lake and several valleys entering 
the mountain regions are more visible in Figure 5 than in 
Figure 4. This example shows clearly the importance of the 
correction for the atmospheric effect in the remote sensing 
data. 
TEMPORAL CHANGES IN CLASS STATISTICS 
Let us consider the temporal changes in the CCT level 
between two Landsat data sets covering one particular 
geographical location ,namely, the Kanazawa area, taken at 
different time. The statistical regions for four typical 
pattern classes are plotted in the feature space of band 5 
and 6 in Figure 6. The solid line elipses represent the 
reliability range of certain pattern classes based on the 
May 23, 1979 Landsat data set. The broken line elipses are 
the same pattern class ranges, except that they are based on 
the Oct.23, 1979 Landsat data set. The center of the elipse 
is given by the mean vector point of a certain pattern class 
and its boundary is drawn with 95 % reliability limit. The