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

tion techniques
al image inter
ring blue for
3S 7/6, and red
enhance faults
rver, provide a
of different
iced from the
s based on the
for lineament
(1) lines of
itinuity which
¡t in images;
of variable
ea immediately
aphic forms;
6) association
and (7) co-
, farms, roads
ctural and/or
an attempt was
ication using
gnition. The
he Landsat MSS
ock types by
eaments and by
actural break
beneath, the
in alternative
Lineaments of the South
Central Alborz Mountains
Monoscopic analysis of computer enhanced
Landsat 2 high pass filtered and stretched band 7
■ — Major fault
—|— Anticline
Linear drainage pattern
Intrusive rock
'f' Volcano
.... Inferred lineament
Figure 3. Lineaments of the southcentral Alborz mountains - scale about 1:750,000.
concept to guide mineral resource exploration and
can be used as a complementary procedure to
geophysical techniques for the purpose of explor
ation. Additionally, this technique of rock discrim
ination could be utilized in order to extend geologi
cal mapping into unmapped and inaccessible areas
within the region.
It should be noted that image classification tech
niques have not been as widely used for geologic
applications as enhancement techniques. This is due
to the fact that classification provides information
on cover conditions and is affected largely by non-
homogenity of geologic units as well as similarity of
spectral signatures of different rock types.
A supervised classification—maximum likelihood
classifier—was used to identify the individual
pixels in the scene. Training sets were delineated
on the computer's color display screen with polygon
programs. The selection of training sets was aided
by field geology information and topographic maps.
Data on the sample means, and variance-covariance
matrices were derived from training set statistics.
Several statistical programs, including training set
check and training set divergence programs, were used
to evaluate spectral separability by creating confus
ion matrices and computing transformed divergence for
each pair of training sets. The transformed diver
gence analysis procedure is described by Haack (1984)
as follows:
Transformed divergence, which is calculated from
the means and covariance matrices of each spectral
class or training site, is a measure of the stat
istical distance between class or site pairs of
interest and provides information on their "separ
ability". This separability is an indirect
estimate of the likelihood of correct classifi
cation between groups of different band combina
tions. Such an estimate provides information
usually obtained by the time consuming and
expensive process of actual classification and
accuracy evaluations. Transformed divergence can