m 
n 
rd 
as 
ie 
es 
1e 
se 
ne 
on 
ce 
es 
ee ET EE 
  
Figure 2. Image 09-08-1992 before (left) and after (right) filtering by the Global Classification Filter 
  
X- Y mn(m ^ n — 2) 
T 3 3 (D 
4m, + nS, m+n 
where À , Ÿ are the mean value of set 1 and set 2, respectively, 
  
2 2 
S, . S5 are the variance of set 1 and set 2, and m, n are the 
number of pixels in set 1 and set 2. Therefore a curve going 
through the T-values is achieved. The extreme values of the 
curve are selected as the boundaries. Thus the groups without 
significant difference are combined. The same procedure is 
performed for the mean value of the original image, the second 
variable of the characteristic space, based on the result of the 
variance grouping. 
The reason for choosing T-test method is that the T-values is a 
good indicator of the selected variables. The bigger the 
difference in mean value between two sets, the larger the T- 
value is. And the smaller the variance in both sets, the larger 
the T-value is. The reason for selecting the extreme T-values 
as the boundaries is that the intermediate T-values represent 
gradual changes, but the extreme T-values indicate the abrupt 
changes of the characteristics of the pixels from one group to 
another. The filter preserves the edge because if the 
characteristic variables describe well the difference of the 
areas on two sides of an edge, the pixels in different areas will 
be grouped into different classes and get different filtered 
values. 
Different T-values describe different ground objects: in the 
tidal flat area, the water surface has high mean and high 
variance because of the alternative bright and dark speckles 
from the moving water surface; in the dry flats the mean and 
variance are all low because of the specular reflection from the 
smooth surface. Grouping the pixels from the same object 
reduces the speckle and have the edges preserved. After 
filtering by the Global Classification Filter, the speckle was 
greatly reduced and the contrast between land and water was 
enhanced (figure 2). 
The real classification of land and water was done by 
thresholding with the help of , from histogram analysis or 
765 
density slicing observation. It was proved that the threshold 
value 1s easier to be determined from the Global Classification 
filtered image than that of the Lee filtered image. The 
continuity of the water or land areas is also improved with our 
filter. The land-water classification map is a binary map from 
which the outline of the areas was extracted for the final water 
lines. 
For the images having difficulty in automated classification, 
the knowledge based screen digitizing was carried out. 
At this moment, the mean and the variance are selected as the 
variables. They may not be the best ones. Later other or more 
characteristic variables may be considered. For programming 
such a filter, the speed and memory of the computer system 
should be well handled because the global strategy requires a 
large amount of memory and computing time. The present 
program run very well on PC both in time and memory. 
2.2 Water surface modeling. Use has been made of the 
"Wadden Model” provided by the National Institute for Coastal 
and Marine Management (RIKZ) (Robaczewska et. al., 1991). 
This model provides the simulation of the tidal water levels 
and the tidal current velocities in the Wadden Sea. It is based 
on calculations involving 26 harmonic components such as 
tidal and rest currents at the North Sea, the climatic conditions, 
water stowage or drawdown as the result of strong wind and 
wave actions, sea floor bathymetry and roughness, the expected 
current drag and the interaction in time between the different 
channel systems, etc. The model yields the data in a grid 
system of 500 m with a temporal resolution of 2.5 minutes and 
simulates the tidal conditions quite well. 
To evaluate the model, the time series data of the Wadden 
Model were compared with the tidal gauging records over the 
same period. The differences, which are not constant, are 
principally a result of local change of wind velocity and wind 
direction. The model takes the wind influence into account 
only when the wind velocity exceeds 8 m/s. 
To adjust these differences between the Wadden Model and 
the real measurements, the model was corrected by the gauging 
data at the time of SAR image acquisition. The differences 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996