×

You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

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

535
jri techniqes
3.3 Study data sets
3.4 Spectral analysis procedure
is not possible,
;ed to represent
jnrponents.
ive transforma-
ition that corn
er dimensions
1 content. This
ance or noise to
rolucci, et al.,
generation of
a describe the
iginal ban-s on
lure allows us
3 contains most
:ion content for
L984)
Four data sets of the classified area were used to
evaluate the contribution of the thermal data in the
multispectral classifiaction. The first data set is
the original seven TM bands. The second set is compo
sed of the same original TM bands excluding the ther
mal band. The third data set is formed by Principal
Components loaded from the original seven TM bands.
The fourthdata set is also Princiapl Components, but
generated from the second data set, i.e. , only the
reflective bands.
The satistics used in calculating the Principal Com
ponents were generated from data samples of the ori
ginal TM data set using every fifth line and fifth
column.
Tables 1 and 2 shpw the statistics for both Principal
Components data sets. Tables 3 and 4 list the eigen-
vales and the corresponding amount of data variance
taht is accounted for by their respective eigenvectors
for both data sets.
A non-supervised approach (Clustering) was selected
to generate the training statistics. This approach
groups spectrally similar pixels regardles of their
spatial position (Tilton and Bartolucci, 1982). and
extracts the maximum quantity of information availa
ble in the TM data.
Eight classifications were carried out in this study.
Only four spectral analysis were conducted, one for
each of the data sets, the classifications are results
of different channel combinations selected after the
analysis procedure (Table 5).
To avoid analysis bias in the generation of training
statistics, the same eight training areas and number
of cluster classes were requested for each of the
four data sets.
The analysis was performed utilizing a defined thres
hold of 1850 for the transformed divergence distance
(D.T.),(Swain and Davis, 1982).
present project
:>er 1982 over
? is 40049-16264
lata used was
icted, i.e.,
isisted of 5,965
The geomtric
ires special
ition of thermal
other TM bands,
al data repre-
Lts from any of
ition of the
agistered grid
ands of the geo-
le same number
Table 1. Eigenvector values for by their respective TM band for the
original seven TM bands (Data set C).
Wavelength Principal Component (Karhunen 6 Loeve) Eigenvector
Band
1
2
3
4
5
6
7
1
0.0376
0.4331
0.5665
-0.1086
-0.1359
-0.6781
-0.0092
2
2
0.0377
0.2641
0.2770
-0.0547
-0.1632
0.4311
0.7988
3
3
0.0293
0.4032
0.3564
-0'0806
-0.0598
0.5898
-0.5930
4
4
0.8109
-0.4312
0.3666
0.0817
0.1167
0.0396
-0.0163
5
5
0.5574
0.4391
-0'5642
-0.0719
-0.4097
-0.0659
-0.0275
7
7
0.1670
0.4115
-0.1578
-0.0465
0.8770
-0.0210.
0.0961
6
6
-0.0101
0.1830
0.0285
0.9822
-0.0272
-0.0116
-0.0013
roximatelly
ssentative of ■
ar features.
i.ch is in south
'45" N and
W to 93°45' W.
to undulating
s and rivers.
7 of a Wiscon-
3 underlain by
P-
was praire gra-
grew along the
Des Moines River,
bodies, agri-
old developments)
dense road net-
ighways).
reservation Ser-
riculture in
al slides for
h slide covers
the ground,
with aerial
tory for Appli-
rdue University
county as re-
e present re-
software system
data is LARSYS
nski,1980).
Table 2. Eigenvector values for by their respective TM band for
the six reflective TM bands (Data set D).
Wavelength Principal Component (Karhunen S LOeve) Eigenvector
1
2
3
4
5
6
1
0.0393
0.4400
0.5694
-0.1389
-0.6792
-0.0092
2
0.0388
0.2654
0.2787
-0.1646
0.4307
0.7986
3
0.0309
0.4096
0.3590
-0.0619
0.5888
-0.5932
4
0.8093
-0.4434
0.3638
0.1190
0.0405
-0.0162
5
0.5591
0.4440
-0.5619
-0.4115
-0.0666
-0.0275
7
0.1686
0.4177
-0.1454
0.8754
-0.0220
0.0960
Table 3. Eigenvalue and the corresponding amount of
variance that is accounted for by their respective
eigenvector for the data set C.
Eigenvector
Eigenvalue
Percent
Variance
Cumulative
Percent
Variance
1
795.642
54.449
54.449
2
554.802
37.967
92.416
3
81.346
5.567
97.983
4
14.888
1.019
99.002
5
10.281
0.704
99.706
6
2.818
0.193
99.899
7
1.482
0.101
100.000
Table 4. Eigenvalue and the corresponding amount of
variance that is accounted for by their respective
eigenvector for the data set D.
Eigenvector
Eigenvalue
Percent
Variance
Cumulative
Percent
Variance
1
795.569
55.706
55.706
2
536.714
37.581
93.287
3
81.290
5.692
98.979
4
10.285
0.720
99.699
5
2.820
0.197
99.896
6
1.482
0.104
100.000