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Figure 1. The Study Site (southcentral Alborz Mountains of northern Iran) 
Landsat MSS Band 5 image - scale about 1:1,600,000. 
The visual image interpretation was accomplished by 
means of a color additive viewer using 70mm black and 
white film positives of the four Landsat MSS bands. 
The interpretation depended upon the evaluation of 
image tone, texture, fabric and relief. Another 
visual image interpretation method was the use of 
Ronchi rulings for analyzing images to identify 
linear features. The Ronchi ruling used in this 
study is a diffraction grating having a spacing of 79 
line pairs per centimeter. When the ruling is 
rotated between the eyes and the Landsat image, lines 
on the image that are perpendicular to the direction 
of ruling are enhanced (by diffraction), and lines in 
other directions become diffuse (Pohn, 1978). 
The computer-assisted image interpretation techniques 
provided superior results to the visual image inter 
pretation techniques. 
Spectral band ratioing was attempted, using blue for 
the MSS band ratio 5/4, green for MSS 7/6, and red 
for MSS 6/5. This technique did not enhance faults 
and other lineaments. It did, however, provide a 
clearer picture of alluvial deposits of different 
ages. 
The second method of analysis was computer-assisted 
image processing of Landsat digital data in which the 
lineaments were enhanced for the purpose of inter 
pretation. In geological analysis, enhancement 
techniques are often performed on band 7 which is the 
preferred near-infrared band. The enhancement 
routines used here are suitable for diverse topo 
graphy and complex structural geology. The enhance 
ment is a form of digital image processing and 
involves the adjustment of brightness value for each 
individual pixel. Potentially useful enhancements 
include contrast stretching (linear, non-linear), 
band ratioing, high pass filtering and diagonal 
derivative processing. The most effective enhanced 
product for this study was computer-enhanced high- 
pass-filtered, contrast-stretched image of MSS band 
7, as shown in Figure 2. This enhancement facil 
itated the interpretation and was especially useful 
in distinguishing between structural features and 
artifacts. 
The lineament map (Figure 3) was produced from the 
computer-enhanced Landsat data and is based on the 
application of the following criteria for lineament 
identification (Short & Lowman, 1973): (1) lines of 
variable length, straightness and continuity which 
are differentiated by tonal contrast in images; 
(2) tonal discontinuities; (3) bands of variable 
width which contrast in tone to the area immediately 
adjacent; (4) alignment of topographic forms; 
(5) alignment of drainage patterns; (6) association 
of vegetation along linear trends; and (7) co 
alignment of cultural features (e.g., farms, roads 
pattern, etc.) with underlying structural and/or 
surrounding topographical control. 
In addition to enhancement algorithms, an attempt was 
made to obtain a lithological classification using 
computer-based spectral pattern recognition. The 
classification routine was applied to the Landsat MSS 
data set in order to discriminate rock types by 
focusing on both sides of major lineaments and by 
attempting to identify whether the structural break 
occurs at near, or at some depth beneath, the 
surface. This discrimination provides an alternative 
Figure 3 
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