Full text: Remote sensing for resources development and environmental management (Volume 1)

543 
Table 3. confusion matrix of classification results 
(2) 
'• s.) 
•ix of clusters 
(3) 
r K 
persion matrix 
(4) 
1 cluster 
different 
the use of the 
nd feature en- 
s are shown in 
expression (1 ) 
age set is the 
e basic image 
cessing. 
iliary height 
lassification 
tion into clas- 
that the deci 
se he mem can 
Cj pure classes mixture classes 
0) 
p ij 
w± 
— 
' G 
-F - 
-c 
"(c)- 
-F - 
c - 
W - 
(C) 
(CJ 
c 
F 
W 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
(F) 
(G) 
(F)|(F) (C) 
(w) 
(c) 
(G,F )(S,F ,W ) 
cultiv. land 
72.« 
il .8 
1 (j. 3 
60.0 
45.7 
43.2 
£#0 
•H 
CD 
j Forest 
,2.2 
72. 1 
5.8 
24. 1 
42.7 
6.8 
water 
5.0 
0.3 
70.9 
3.6 
0.7 
40.1 
grass 
3.! 
10.0 
0 
3.4 
6.1 
0 
-p 
orchards 
2. 0 
2.3 
1.0 
6.1 
3.7 
0.5 
3 
0 
settlem. Ian 
3.., 
» 
0 
2., 
0.4 
0.5 
43 
-P 
bear land 
" 
0., 
0.7 
0 
? 
area 
3822 
4042 
104 
6403 
3440 
220 
'S 
cultiv. land 
80.5 
8.6 
0.9 
0 
,2.5 
5b. G 
50.1 
26.0 
62.3 
29.0 
30.2 
61 3 
•H 
Forest 
7.0 
78.0 
0 
34.7 
47.1 
26.8 
41.9 
64.7 
6.2 
0 
27.6 
10.9 
-p 
j water 
4.3 
0.2 
90. 1 
0 
0.2 
2.8 
0.7 
0.5 
23.7 
66.4 
0.8 
ine 
bO 
l grass 
2.8 
7.0 
^“5 
02.7 
32.6 
3.2 
1.9 
2.0 
3.4 
0.9 
29.1 
10.3 
•H 
a> 
i orchards 
2.7 
2. 1 
n 
0 
4.5 
6.4 
3.3 
2.8 
0.6 
0 
5.5 
2.8 
43 
settlem. Ian 
1.7 
0 
0 
0 
0 
2. 1 
0.8 
0.2 
3.9 
3.7 
0 
0 
43 
-p 
bear land 
0 
3.5 
0 
2.7 
3.1 
°-' 
1.4 
2.9 
0 
0 
0.8 
0 
area 
4075 
230, 
9, 
1275 
0739 
855 
3772 
355 
107 
254 
604 
&i (x) = + - 2 Inpc^«) (5) 
Where, the P(W{) represents a priori probability of 
a feature class (W*), which is usually estimated by 
the area percentage of class (Wi) in whole study area 
(see the figures in last line of table 1). However, 
the a priori probabilities of ground feature in dif 
ferent hieght range are different in practice (see 
table 1). So the better classification result can 
only be got when P (Wt) is estimated in certain 
ground height range and the classification is per 
formed within the image area corresponding to the 
same height ra.nge . 
In our experiment, the study area was first digitals 
iy segemented into different height range area by 
taking- the "density (height)" sliced DTM image as 
masks. Then the classification was performed se- 
perately in different height range areas. Finally, 
the results from them were digitally mosaiced each 
other and forming- resulting 1 classification image. 
Table 3 shows the confusion matrix of classification 
seperately without (upper block) and with (lower 
block) introducing ground height information. From 
the table we can find that the classification ac 
curacy was improved by 8% for cultivated land when 
height information was introduced. 
(2) Itsuhito Ohnuki (1981 ): Terrain Effect Nor- 
marization Method of landsat Data and its Efficiency 
of Forest Type Glassification, Forestry and Forest 
Production Institute, P.0. Box 16, Tsukuba Norin- 
kenkyu-danchi, Ibaraki 305» Japen. 
(3) J.A. Richards, D .A. landgrebe, P.H. Swain (1982) 
A Means for Utilizing Auxiliary Information in Mul- 
tispectral Classification. R.S. of Eavironment, 12, 
463 - 477. 
(4) Yang Kai, Lin Kaiyu, Chen Jun, Lu Jian (1985) s 
A Classification Scheme of landsat Multitemporal 
Feature Images with the Use of Auxiliary DTM Data. 
Acta Geodetica et Cartographies Sinica, Vol. 14» 
Ho. 3 China. 
5* Conclusions 
Based on above classification processing, the areas 
of defined ground feature classes in the testing- 
county was calculated and compared with the existing 
findings. The results shown that about 30% of cul 
tivated land area in the county was not taken into 
account in the existing findings. So that, we can 
concluded that by elaborate classification scheme, 
particularly by introducing ground height information 
into classification procedure, the LANDSAT MSS images, 
although whose geometric resalution is origionally 
not high enough, can satisfyingly be used to verify 
the lack fidelities of existing findings to cultiva 
ted land in county level. 
References 
(1 ) J.T. Tou, Gonzalez (1 974): Pattern Recognition 
Principles, Addison-Wesley Publishing Company.
	        
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