a ^c» & Q 
land cover types have not aggregated in the first several 
clusters(table 2) but separately distributed. Having 
identified the corresponding land cover type of these 
peaks, it is convenient to evaluate and label surrounding 
'non-peak' clusters. The extracted rice area accuracy 
with strategy B is over 84% compared with the statistics 
obtained from the Agricultural Investigation Team 
affiliated to the Agricultural Bureau of Hubei province. 
4. DISCUSSION 
In supervised classification, the spectral characteristics 
of the training sites are used to "train" the classification 
algorithm for eventual land cover mapping of the 
remainder of the image(Jensen, 1986). Once 
representative training sites selected, multivariate 
statistical parameters calculated from each training site 
are used to evaluate every pixel. The pixel is assigned to 
the class of which it has the highest likelihood of being a 
member no matter it is within or outside the 
administrative boundary. As far as strategy A and 
strategy B are concerned, there is not much difference 
between these two strategies for supervised 
classification. 
In a unsupervised classification, the computer is allowed 
to select the class criteria such as means and covariance 
matrices(Jensen, 1986). In strategy A, class criteria are 
calculated within the administrative boundary only 
while it is done within the whole circumscribing 
rectangle in strategy B. Certainly, diverse results will 
emerge for unsupervised classification with different 
strategy. 
In image data, the spatial dependence among pixels is 
the fundamental aspect of spatial pattern(Henebry, 1993). 
If the satellite image was masked with boundary pixels, 
information about this dependence is lost at all(Henebry, 
1993). According to this rationale, contrary to the 
method used by many authors, strategy B is used in our 
project. Statistical unsupervised classification work was 
243 
performed firstly and then mask out the uninterested 
area outside the administrative boundary. By this way, 
we hope, the spatial dependence can be retained 
especially when it is concerned with the rice theme. Our 
results suggest that strategy B is practical in rice 
identification and obviously excellent than strategy A 
for unsupervised classification. 
In the above discussion, no matter which strategy was 
used, the deviation induced by the boundary pixels 
should be noticed. Readers can refer to Crapper(1984) 
on how to calculate the boundary pixels. Rao and 
Mohankumar(1994) explained in detail the effect of 
spatial resolution and the percentage of boundary pixels 
on accuracy of area estimation. In general usage, 
boundary pixels when occupying only a little percentage 
of the total study area(0.442 96, in our experiment), can 
usually be ignored in acreage estimation. 
For visual interpretation, uncertainty in assigning a 
theme to a grid cell can occur when the grid cell lies near 
or on the boundary of a region(Crapper, 1984). 
According to this rationale, strategy B should also be 
considered in visual interpretation. That's to say, first, 
conduct the visual interpretation work and then mask out 
the uninterested area and calculate the labeled results. 
5. CONCLUSIONS AND FURTHER RESEARCH 
For strategy A, we only give the result of pre-classified 
50 clusters. Further labeling and interpretation work 
haven't been done. Surely, it can be done, but certainly, 
it will be more complex than strategy B and the accuracy 
won't be satisfying. 
In our test, the study area was also classified into 70 
clusters for strategy A and strategy B in unsupervised 
classification. Similarly, with strategy B, the formerly 
classified 70 clusters can be more easily reclustered into 
10 types than with strategy A. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996