47 
4-1. Preliminary processing of satellite image data 
(1) Cloud area and land area 
We excluded the clouds and lands to extract the study sea area. The identification of clouds 
and lands area are easily distinguished among the visual, near infrared and temperature 
distribution data. 
(2) Discussion on smoothing method 
For some cases, large difference was observed between the adjoining pixels in the 
temperature distribution. It is considered that this is due to the influence of thin cloud and vapor 
or the noise occurred during the data acquisition.Therefore, we discussed the method to 
remove these noises by smoothing processing. As for smoothing processing, we employed the 
smoothing matrix (low pass filter 2 x 2 - 7 x 7) to temperature distribution data. And,for some 
cases,we applied ABIC minimization method to smoothed data. 
4-2. Extraction of tidal front 
We examined the three different methods for the water block classification.In the first 
method,the temperature distribution data of NOAA image is clasified by applying single band 
clustering and level slicing. In the second method,ISODATA method clustering is carried out to 
the multi-band data (i.e. visual, near infrared and temperature distribution).In the last 
method,the maximum likelihood method is applied to the multi-band data. The borderline of the 
classified water blocks was regards as the tidal front and used for the discussion. 
4-3. Discussion on correlation 
For the discussion, we chose the estimated tidal front,which corresponds to the sea 
truth,from the borderlines of classified water blocks. Comparison between the estimated tidal 
front and sea truth was carried out by using the distance average.Distance average was 
calculated by averaging the distance of both front from north to south.The distance in the 
direction of east to west is used for the calculation. 
5. Results and consideration 
5-1. Preliminary processing of satellite image data 
(1) Removal of clouds and lands area Table 2:The data and CCT values with distinction of clouds and lands 
Clouds were extracted with relatively 
high accuracy But,for some cases, 
distinction of land and sea area was 
difficult,because the both areas had the 
same CCT values. In this study, we 
established the threshold value from the 
histogram of each data by human 
judgment. There remained some issues 
yet to be solved in objectivity and 
processing time because it was 
necessary to establish the threshold 
value respectively for each data. Table 
2 shows the data and CCT values with 
distinction of clouds and lands. 
Day of 
observation 
Lands 
Clouds 
Data*l 
CCT 
values 
Data*l 
CCT 
values 
1992 May 21 
N 
64- 115 
V 
84 - 255 
22 
N 
57 - 108 
V 
90 - 255 
24 
V 
54- 76 
V 
100 - 255 
25 
V 
60- 90 
V 
120 - 255 
1991 July 21 
N 
60- 130 
V 
100 -255 
22 
N 
50- 130 
V 
100-255 
23 
V 
0- 85 
V 
121 -255 
T 
207 - 255 
N 
120 -255 
T 
0- 35 
24 
V 
0- 80 
N 
100 - 255 
1992 Aug. 28 
N 
50- 70 
V 
61 -255 
29 
N 
53 - 80 
V 
74 - 255 
30 
N 
53 - 86 
V 
76 - 255 
Sep 1 
N 
66- 79 
V 
81 -255 
1991 Sep 1 
N 
53 - 105 
V 
90 - 255 
2 
N 
67- 105 
V 
110-255 
3 
N 
50- 100 
V 
83 - 255 
5 
N 
61 - 100 
V 
77 - 255 
6 
N 
57- 95 
V 
67 - 255 
* 1 V: visual data 
N near infrared data 
T temperature data