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Title
Remote sensing for resources development and environmental management
Author
Damen, M. C. J.

16
the expression of topography; some make sharp contrast
between cover types.
Another enhanced images were created by spatial
filtering. In order to reduce all the sources of noise
and propagation fluctuations, the moving average
filter was employed for this effort. Eight original
bands data were put into an IDIMS function called
AVERAGE. The spectral responses for 3 X 3 pixels were
averaged with an input mask of coefficients to be
moved over the entire array of data.
Besides the enhancement techniques mentioned in
previous paragraphs, bilinear interpolation algorithm
was applied to the 8 original bands data and several
selected images. In general, resampling accomplishes
two things: (1) It relocates the pixels to more
accurate positions. (2) It can smooth out the blocky,
mosaiclike pattern that results when individual
pixels become visible.
The analytical technique, that was used to process
the airborne MSS data, was hybrid supervised/unsuper
vised maximum likelihood pattern recognition approach.
Experience has shown the desirability of using both
supervised and unsupervised approaches together in a
procedure (Fleming et al., 1977).
2.3 Classification procedures
Classification is the process of assigning each of
the pixels of the airborne MSS data of the study area
to a class based on the set of input statistics
generated by training area and cluster formation.
Eight original bands of airborne MSS data were sub
jected to standard clustering and classification
techiques. On grouping of training samples into
meaningful classes, sixty training areas which were
selected by their uniformity of light radiance using
function TR, TA and IRRECV in IDIMS. The spectral
variation is caused by different vegetation commu
nities present and the presence of environmental
parameters such as tree species, tree age, slope and
aspect which are found to influence the reflectance
values of vegetation cover types (table 2,3).
Table 2. Age class of forest plantations.
Class
Age (year)
1
1 - 6
2
7-15
3
16 - 25
4
25 - 40
5
40 - 60
6
over 60
Table 3. Slope class
of forest stands
Class
Slope
(%)
1
0 -
5
2
6 -
15
3
16 -
30
4
31 -
45
5
46 -
60
6
61 -
75
7
over
75
After the data of training areas were collected,
they were then put into an IDIMS function entitled
ISOCLA. The ISOCLA algorithm in IDIMS used a com
bination of distance measurements and statistical
parameters to distinguish differences between
classes. This type of procedure was used to train
the maximum likelihood classifier of IDIMS. The
maximum likelihood classifier was used to determine
the statistical parameter which characterizes the
classes (Walsh, 1930).
2.4 Best band combinations
3 RESULTS
The best band combinations were selected from 8
original MSS data, ratio images, principal component
transformations, spatial filtering data, resampling
data and mixed bands. The symbols of above mentioned
data are designated as follows:
CnB: A band combination which is composed of selec
ting n bands from the original 8 bands. n=l,2,...,8.
It may represent one of the 8 bands or 2 or more bands
combination. For instance, the symbol C6B represents
the band combination that is composed of 6 bands out
of 8 bands available. There are 28 possible combina
tions of C6B. Among them, the band 5, 7, 8, 9, 10, 11
combination is the best because it has the highest
value of average divergence. The total band combina
tion of CnB is 255.
CnB'R: A resampling band combination of CnB.
PmB: A band combination which is composed of selec
ting m bands from 3 principal component transforma
tions (PCj, PC 2 , PC3). m=l,2,3.
PmB*R: A resampling band combination of PmB.
RnB: A band combination which is composed of selec
ting n ratio images from previously stated 8 ratio
images n=l, 2 8.
RnB-R: A resampling band combination of RnB.
AnB: A mixed band combination which is selected from
2 best band combinations from CnB; 3 principal com
ponent transformations and 3 best band combinations
from RnB. i.e., band 5, band 9, PC^, PC 2 , PC3, 10/8,
7/9, and 5/11. n=1,2,...,8.
AnB'R: A resampling band combination of AnB.
AVnB: A spatial filtering band combination of CnB.
AVnB’R: A resampling band combination of AVnB.
In best band combination selection, average diver
gence and minimum divergence are used as the ranking
criteria. By the ranking of divergences, 47 best band
combinations were selected from above mentioned
various combinations. Means, variances and covariance
matrices of these band combinations were input to a
maximum likelihood classifier which was used to
characterize the forest cover types.
2.5 Accuracy assessment
The most common way to represent the accuracy of a
airborne MSS data classification is in the form of
an error matrix. An error matrix is a square array
of numbers set out in rows and columns which express
the number of pixels assigned as a particular land-
cover type relative to the actual land cover as
verified in the field or from interpreted aerial
photographs. The columns usually represent the
corrected reference data and the rows indicate the
computer assigned land-cover classes.
Once the error matrix has been generated, both
percent accuracy and mapping accuracy are computed.
The percent accuracy is shown as:
number of correctly
pixels in a class
total number of
pixels in that class
It is a measure of the number of pixels that were
correctly classified within a particular surface
cover type classification delineation. But it is
somewhat inadequate for the expression of the error
in class location on a map. Such error must combine
the commission and omission in one summary measure
since it must reflect the change in class pattern
due to losses (omissions) to, as well as additions
(commissions) from other classes (Kalensky et al.,
1975). Consequently, it is proposed the mapping
accuracy as a new measure.
N T
percent accuracy (%)
100
mapping accuracy (%) =
N I +E I
X 100
wljere
Nj.: number of correctly classified pixels in
class I
Ej.: number of erroneous pixels in class I
(sum of omissions and commissions)
3. 1 Classifii
The imageries
approximate!}
plantations,
and miscellar
According to
ness, 29 cove
study area. 1
the ISOCLA fu
the same cove
distance were
a transformed
together. 29
(table 4). Th
were STDMAX:4
ISTOP:10.
Among the 2
classes. Then
in IDIMS were
study area in
3.2 Best band
As previously
Table 4. 23 c
Class
No.
Code
1
A
C:
P-
2
3
C
D
C]
P-
C1
pj
4
E
Ci
pj
5
G
Ne
6
H
Me
7
I
Na
8
J
Ta
Pi
9
K
Mo
10
M
Ta
re
11
N
Ta
re
12
0
Mo
13
P
Ma
14
Q
Na
15
R
Nui
16
S
Pai
17
T
Mil
pie
18
u
Chi
pie
19
V
Taj
cec
20
w
Cor
woe
21
X
Mix
22
Y
Con
woo
23
Z
Mix