Table 1. a priori probability p (i) of different classes in different 
height ranges 
classes 
r V' 
2 
3 
4 
— 
8 
? 
P(h) 
height 
probability p(i; 
(c) 
(0) 
(F) 
(B) 
(w) 
(S) 
(G) 
(1) 
0m~20 ,n 
86 
■ ! ■ 
1 
11 
1 
- 
11 
(2) 
21 ni—50i>i 
67 
4 
18 
4 
5 
3 
34 
(3) 
51 m~IOOni 
« 
6 
45 
2 
2 
2 
1 
28 
(4) 
101 ■—200>ii 
21 
1 
6» 
4 
• 
4 
17 
(5) 
20l">~900»> 
13 
5 
51 
26 
8 
10 
entire 
county P( W-^) 
46 
4 
36 
6 
3 2 
1 
100 
Table 2. calculation results for feature image selection 
sequen 
feature images 
deraen- 
t ion 
X', 
X, 
X, 
nr 
J ■ 
/. 
1 
BI089 
2 
1.14 
0.51 
- 
- 
1.65 
2 
RT89 
2 
1.67 
0.11 
- 
1.08 
3 
KL89 
4 
1.77 
1.10 
0.73 
0.09 
1.86 
1.83 
4 
B79 
4 
1.14 
1.08 
0.85 
0.59 
1.73 j 
1.94 
5 
B?8 
4 
1.62 
1.13 
0.93 
0.44 
2.06 | 
2.06 
distinguished in advance. The a priori probabilities 
of different classes (cultivated land (C), orchard 
land (0), forest land (f), grass land (G), water 
area (w), settlement area (S) and bear area (B) are 
listed in table 1 . 
The class attributes of each sampling pixels were 
encoded by class numbers and imput to computer 
system for forming ground truth sampling images. 
3. Selection of feature image-s 
Before classification action, an important prepro 
cessing is so-called feature selection. To this end, 
some image transformations in multispectral domain 
should be performed, and certain criterion for 
feature selection should be defined. 
In our experiment following alternative feature 
images had been prepared by digital image transfor 
mation techniques. 
(a) B78 origional four bands' LANDSAT MSS 
image set corresponding to the year 1978; 
(b) B79 origional four bands' LANDSAT MSS 
image set corresponding to the year 1979; 
(c) KL89 two-temporal princple component 
image set, with four bands images seperately cor 
responding to the 1st and 2nd principle components 
of origional B78 as well as B79 image sets; 
(d) RT89 two-temporal ratio image set, with 
two bands' images seperately corresponding to the 
ratios of band 7 to band 5 of origional B78 as well 
as B79 images; 
(e) BI089 two-temporal biomass index image 
set, with two bands' images seperately corresponding 
to the Biomass Index transformations of origional 
B78 as well as B79 images. 
In order to selecte optimum one from above alter 
native feature images, the "dispersion matrix 
criterion" was used in our practice: 
if J(i) = min { J(B79),..., J(BI089) } . (1 s 
then (i) is the optimum feature image ' ' 
J = tr (Si' • S, ) = Z Aj (2 ) 
Xj — eigenvalue of matrix ’ S,) 
S, internal dispersion matrix of clusters 
S » = ]r ^ x'sojn ^ ) ( X —Mk Y J ( 3 ) 
k cluster number 
X intensity vector 
Mk expected vector of cluster K 
Si general (or external) dispersion matrix 
of clusters 
Si= ? (Xj-AKXt-s.y (4) 
1 feature point number 
Jt, expected vector of general cluster 
Each J (i) can be calculated based on different 
alternative feature images, and with the use of the 
image part corresponding to real ground feature en 
coding images. The calculation results are shown in 
table 2. According to the criterion (expression (1 ) 
we can find that the BI089 feature image set is the 
optimum one. So it was selected as the basic image 
data for following classification processing. 
4.-Classification processing with auxiliary height 
information 
An efficient strategy for improving classification 
is to introduce ground height information into clas 
sification process. It is well known that the deci 
sion function of a maximum likelyhood schemem can 
be expressed as 
whe re