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The spatial information provided by the first principal component is very stable if compare to the spectral contact of the
original TM bands. The PCA merger needs further investigation because the spatial information is non-proportional
retransformed to the different spectral bands (Chavez et al., 1991).
4 DISCUSSION AND RESULTS
An important disadvantage of each of the image merging procedure is its inability to combine dissimilar spectrally and
not simultaneously collected data. This is especially significant in the case of monitoring the open-cast mine area.
Because of continuos mining operation the shifting in time between satellite and airborne sensor registrations should be
as narrow as possible. If not, the changes caused by mining operations might be so significant, that the spatial and
thematic content of satellite and aerial images became non-comparable. Landsat TM and aerial photographs used in our
study have been collected within one month. Although some differences 'one can observed, they are not essential for
merging effect.
For geologic remote sensing the fusion of visible bands and high resolution spatial data is limited to color differences of
the lithological units. A significant improvement in overall interpretability of merged images is observed when the
infrared bands together with visible are incorporated into the final product. That is because of unique spectral
reflectance and absorption features of different geological units, in infrared region. Particularly within short-wave
infrared (SWIR) region these features can be directly related to distinctive mineralogical properties (Hunt, 1977, 1979;
Hunt and Asley, 1979; Grasso, 1993). Within the SWIR part of electromagnetic spectrum most geologic materials have
reflectance maxima near 1.60 pm wavelength (Prost, 1980). Phylosilicate minerals such as clays have additionally
reflectance minima centred at 2.20 pm wavelength. This is due to absorption effect in 2.10 to 2.35 um region (Prost,
1980; Goetz et al., 1983) and sensitive of this absorption features was also demonstrated (Hunt and Ashley, 1979). This
is the reason of widespread use of infrared part of the electromagnetic spectrum in geologic remote sensing (Rowan et
al., 1974; Abrams et al., 1977, 1983; Geotz and Rowan, 1981; Lang et al., 1984a, 1984b; Yamaguchi, 1987; Campos-
Marquetti and Rockwell, 1989; Pontual, 1989; Grasso 1993).
In general, the results of the study presented corroborated the specific spectral properties of SWIR region. A visible
interpretation as well as the formal assessment using statistic approach has been indicated that the infrared bands of the
Landsat system, particularly SWIR channels (TM 5 and TM 7) contained a great amount of geological information.
Statistical-analysis method which was used to rank the 20 possible three band combinations based on the Optimum
Index Factor (OIF) (Chavez et al, 1982). The OIF value for any of three bands subset based on the sum of their
standard deviations normalised by the sum of absolute values of their correlation coefficients, In this case the largest
OIF value indicates the biggest amount of information contained in a particular three band set and the least amount of
duplication. Figure 6. shows that, high rankings were obtained by three-band combinations that included one or two of
visible band (TM 1 and TM 3) and one or two infrared bands (TM 4 and TM 5). The next three composits contained of
TM 3, 4, 5 and 7 bands had also high OIF values, while the relatively smaller OIF values characterised four composites
that included TM 1, 2, 3, 4 and 5 bands.
Explicitly low rankings were obtained for the TM 1, 2, 3 and 4 band combinations, while the middle rankings were
observed for the TM 1, 2, 3, 4 and 7 bands combinations.
30
27
24
21
k123 k124 1294 k134 k127 237 k247 k137 \@47 k147 K257 k125 k235 K246 K357 k457 k157 ki35 k345 k154
Figure 6. The OIF (Optimum Factor Index) values for three-band original Landsat TM bands combinations
The same procedure was performed to determine the amount and distribution of information contained in the three-pair
subsets after merged of Landsat TM and aerial photographs. In order to determine the OIF values the different fusion
methods were assessed taking into account a total variance of each data subset. For the six subset of Landsat TM bands
combinations (TM 123, 134, 234, 145, 247, 457) the rankings of using merging procedures in shown on Figure 7. One
can easily observed that the HPF (High Pass Filter) method characterised the maximum of OIF values for the full data
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 923