Cl PA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey 
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• relief 1: remains of building alignment; 
• between reliefs 2 and 3: zone of tabernae; 
• relief 5: possible prolongation of the Paredejas with 
orientation parallel to the traces discovered in 1995. 
MICRORELIEVES 
i 1 i 2 l 3 i 4 I s 
Figure 6. Micro-reliefs profile in the zone between Casa de 
Taracena and Termas de Los Arcos (direction N-S). 
Figure 7. The detected urban areas. Three principal alignments 
of buildings are probably identified by DTM micro 
reliefs. These alignments fit with Casa De Taracena 
orientation. 
In conclusion, the altimetric data provided by DTM gives some 
interesting traces about building locations; however the quality 
of this DTM, estimated with photogrammetric methodology, is 
not so good to identify all the micro-reliefs, but only the most 
evident. We could identify and suggest an optimal solution by 
using airborne laser scanner DTMs, currently not available on 
this site. 
3. FEATURE EXTRACTION 
3.1 Image filtering 
The process of image acquisition frequently leads 
(inadvertently) to image degradation. Due to mechanical 
problems, out-of-focus blur, motion, inappropriate illumination, 
and noise the quality of the digitized image can be inferior to 
the original. The goal of enhancement is, starting from a 
recorded image c[m,n], to produce the most visually pleasing 
image a[m,n\. The goal of restoration is, starting from a 
recorded image c[m,n], to produce the best possible estimate 
a[m,n\ of the original image a[m,n\. The goal of enhancement is 
beauty; the goal of restoration is truth. Obviously, for photo 
interpretation purposes, restoration is not necessary but 
enhancement makes observer work quick. 
We have performed image enhancement and filtering by using 
RSI ENV1 software. 
3.1.1 Histogram stretching 
Frequently, an image is scanned in such a way that the resulting 
brightness values do not make full use of the available dynamic 
range. This can be easily observed in the histogram of the 
brightness values shown in fig. 8. By stretching the histogram 
over the available dynamic range we attempt to correct this 
situation. If the image is intended to go from brightness 0 to 
brightness 2 B -1, then one generally maps the 0% value (or 
minimum) to the value 0 and the 100% value (or maximum) to 
the value 2 b -l. The appropriate transformation is given by: 
b\m,n] = (2*-l L 1 J [•] 
v ’ max-min 
where a[m,n] is the original pixel value and b[m,n] is the 
stretched pixel value. 
File 5tretch_Type Histogram_Source Defaults Options Help 
Figure 8. Image histogram Gaussian stretching. Note input and 
output histograms. 
Figure 9. Original grey scale image of the test area between 
Casa de Taracena and Termas de Los Arcos. The 
building alignment {tabernae).