18 
Table 7. Ranks of 47 best band combinations 
Percent 
Mapping 
KHAT 
CPU 
Code 
Band combination 
accuracy 
accuracy 
value 
time 
Rank 
(%) 
(%) 
(min. ) 
AV8B-R 
4,5,6,7,8,9,10,11 
98.64 
97.45 
.982865 
160 
2 
C8B-R 
4,5,6,7,8,9,10,11 
98.46 
97.09 
.980647 
155 
1 
C7B-R 
5,6,7,8,9,10,11 
97.13 
94.92 
.963907 
129 
3 
C6B-R 
5,7,8,9,10,11 
97.00 
94.87 
.962316 
109 
4 
AVÖB 
4,5,6,7,8,9,10,11 
96.92 
94.52 
.961431 
155 
6 
AV6B 
5,7,8,9,10,11 
96.22 
93.58 
.952531 
109 
4 
AV7B 
5,6,7,8,9,10,11 
95.54 
92.69 
.943983 
129 
7 
R8B-R 
10/8,10/6,7/9, 
9-6/946,10/5, 
9/11,4/7,5/11 
95.24 
92.24 
.940128 
158 
8 
A8B-R 
PC,,PC 2 ,PC 3 , 
94.10 
91.42 
.925931 
186 
11 
10/8,7/9,5/11, 
5,9 
AV5B 
5,7,8,9,11 
94.05 
90.78 
.925385 
90 
9 
C5B-R 
5,7,8,9,11 
93.92 
90.44 
.923883 
90 
9 
C4B-R 
5,8,9,11 
92.89 
89.03 
.911200 
70 
12 
AV4B 
5,8,9,11 
91.53 
87.35 
.894271 
70 
13 
C8B 
4,5,6,7,8,9,10,11 
89.08 
82.87 
.865814 
150 
16 
C7B 
5,6,7,8,9,10,11 
88.35 
81.99 
.857194 
125 
15 
A3B 
PC!,PC,,PC,,10/8, 
7/9,5/11,5,9 
88.10 
82.70 
.852243 
181 
17 
C6B 
5,7,8,9,10,11 
86.99 
80.35 
.840499 
105 
14 
C5B 
5,7,8,9,11 
85.56 
78.72 
.822961 
85 
18 
R3B 
10/8,10/6,7/9, 
9-6/946,10/5, 
9/11,4/7,5/11 
84.82 
77.50 
.812077 
153 
19 
A7B 
PC,,PC,,10/8,7/9 
5/11,5,9 
84.22 
78.26 
.804332 
157 
20 
P3B-R 
PC,,PC,,PC, 
5,8,9,il 
83.87 
81.76 
.798504 
62 
21 
C4B 
83.21 
76.57 
.795936 
65 
22 
A6B 
PC 2 ,10/8,7/9, 
5/11,5,9 
82.28 
77.46 
.780216 
136 
23 
A5B 
PC,,PC,,10/8,• 
7/3,9 
81.27 
75.36 
.766500 
116 
24 
C3B-R 
6,8,9 
80.39 
74.55 
.758890 
47 
25 
P3B.A3B 
PC,,PC,,PC 3 
10/8,10/6, 
79.76 
74.69 
.752586 
60 
26 
R7B 
79.25 
74.09 
.743437 
128 
28 
9-6/946,10/5, 
9/11,4/7,5/11 
R6B 
10/8,7/9,10/5, 
9/11,4/7,5/11 
78.22 
72.04 
.731961 
107 
27 
A4B 
PC,,PC,,7/9,9 
76.58 
74.63 
.711602 
96 
31 
AV3B 
6,8,9 
75.70 
69.41 
.703845 
47 
29 
R5B 
10/8,7/9,9/11, 
4/7,5/11 
75.45 
70.61 
.697990 
88 
30 
R4B 
10/8,7/9,9/11, 
4/7 
74.31 
69.96 
.684153 
67 
34 
P2B 
PCj.PC, 
10/8,7/9,5/11 
73.66 
66.56 
.678652 
60 
33 
R3B 
73.61 
70.53 
.675170 
46 
32 
AV2B 
5,9 
68.94 
63.93 
.624756 
31 
35 
A2B 
PC,,7/9 
67.61 
67.95 
.607137 
60 
37 
C3B 
6,8,9 
65.82 
57.01 
.599942 
45 
36 
C2B-R 
5,9 
64.18 
63.06 
.572122 
31 
38 
C2B 
5,9 
54.05 
48.29 
.473751 
30 
39 
R2B 
10/8,7/9 
50.16 
43.28 
.434277 
31 
40 
AV1B 
6 
48.42 
51.82 
.373982 
15 
41 
C1B-R 
6 
46.38 
54.41 
.353796 
16 
42 
PIBjAlB 
PC! 
44.57 
41.42 
.357885 
30 
43 
C1B 
6 
41.59 
42.82 
.321694 
15 
44 
RIB 
7/9 
29.22 
29.98 
.228814 
15 
45 
Their ranks were determined by average divergence 
first and then minimum divergence. For example, among 
the C2B's 28 possible band combinations, the combina 
tion of band 5 and band 9 is the best because it 
possesses the highest average divergence, 1790 (table 
&). The C5B represents a band combination with 5 
bands. Among their 56 possible band combinations, the 
combination of band 5, 7, 8, 9, 11 and combination of 
band of 5, 7, 8, 10, 11 possess the same average 
divergence. According to their minimum divergences, 
we ranked the former first, the latter second. 
As previously mentioned, on land cover types classi 
fication accuracy assessment, error matrix was used. 
By the error matrix of each band combination, the 
percent accuracy, mapping accuracy and KHAT value 
were computed (table 7). The KHAT value is a measure 
of agreement which represents how well the classifi 
cation data agree with the reference data (Congalton 
et al., 1983). 
Table 8 shows the CPU time of various operations. 
Generally speaking, the computer time required for 
the classification increases approximately as double 
of the number of band used. On the best band combina- 
Table 8. CPU time of various operations 
Operation 
CPU time 
(min.) 
Spatial filtering 
5 
Resampling 
5 
Ratio images 
3 
Principal component transformation 
15 
Classification with 8 bands 
150 
Classification with 7 bands 
125 
Classification with 6 bands 
105 
Classification with 5 bands 
85 
Classification with 4 bands 
65 
Classification with 3 bands 
45 
Classification with 2 bands 
30 
Classification with 1 bands 
15 
tion selection, computer time needed should be the 
top consideration. For instance, according to the 
testing of significance, there is no siginficant 
difference between AV8B-R and C8B-R. But AV8B-R spent 
more CPU time on data processing, therefore C8B*R 
ranked first, and AV8B-R second. 
4 CONCLUSIONS 
Bearing in mind the objective to find the optimal 
band combinations of airborne MSS data for forest 
cover types classification, the following conclusions 
are drawn. (1) The result has shown that the past 
experience, maximum accuracy in cover type classifi 
cation can be obtained by using all wavelength bands 
available, is true. From table 7, one can find that 
the best band combination contains more bands. 
(2) Resampling and spatial filtering techniques 
increase 7-10% classification accuracy because both 
of them have the ability in sharpening the images to 
approach a better resolution. (3) Principal component 
transformations seem no effect on improving the 
accuracy of forest cover types classification because 
they are less effective on contrast enhancement for 
vegetations. (4) Ratio images are also not good enough 
for forest cover types classification because the 
ratio images tend to suppress the difference in 
albedo. Thus dissimilar materials with different 
albedos may be inseparable on ratio images. (5) Six 
out of eight components of mixed bands are unable to 
improve classification accuracy. The mixed bands 
perform naturally insufficiently. 
ACKNOWLEDGMENTS 
The authors would like to express their appreciation 
to the Remote Sensing Planning Committee, Council of 
Agriculture, Republic of China, for providing the 
financial support. Thanks are also due to the Center 
for Space and Remote Sensing Research, National 
Central University, for data processing services. 
REFERENCES 
Congalton, R.G.; R.G. Oderwald; R.A. Mead 1983. 
Assessing Landsat classification accuracy using 
discrete multivariate analysis statistical tech 
niques. Photogrammetric Engineering and Remote 
Sensing, Vol. 49, No.12, Dec. 1983, pp.1671-1678. 
Fleming, M.D.; R.M. Hoffer 1977. Computer-aided 
analysis techniques for an operational system to 
map forest lands utilizing Landsat MSS data, LARS 
technical report 112277, Purdue University, West 
Lafayette, Indiana, U.S.A. pp.1-231. 
Kalensky, Z.; L.R. Scherk 1975. Accuracy of forest 
mapping from Landsat Computer Compatible tape. 
Proceedings of the 10th international symposium on 
Remote Sensing of environment. PP.1155-1163. 
Walsh, S.J. 1980. Coniferous tree species mapping 
using Landsat data. Remote sensing of Environment 
9. PP.11.26. 
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