4 RESULTS AND DISCUSSION
4.2 Reection intensity spectra
4.2.1 In the pure environment
Firstly, let us take a glance at pure, not patterned materials, coloured in olive drab colour. Fig. 4.3 shows us the comparison of normalized reection spectra of nylon, eece (polyester) and cotton-polyester fabrics.
Figure 4.3: Normalized reection spectra of monochromic samples.
The shapes are not important much now, as are not being a scope of our investi-gation. Let us calculate characteristic numbers for the samples. When calculating, it is better to use normalized data than non-normalized, as described in part 3.8, although the second ones can also be used for comparison in our case.
Table 7 shows characteristic numbers for monochromic nylon (N), eece (F) and cotton-polyester (CP) fabrics samples.
Now let us nd out the similar characteristic numbers for all the three patterned
Table 7: Characteristic numbers for monochromic samples.
λ, nm 750 850 950 1050 1150 1250 1350 Fleece 0.330 0.293 0.342 0.316 0.278 0.292 0.416 CP 0.198 0.330 0.613 0.586 0.506 0.532 0.572 Nylon 0.296 0.394 0.450 0.505 0.537 0.561 0.593 samples. Fig. 4.4 shows us the spectra of these samples.
Figure 4.4: Normalized reection spectra of patterned samples.
Table 8: Characteristic numbers for patterned samples.
λ, nm 750 850 950 1050 1150 1250 1350 ACUPAT 0.259 0.386 0.524 0.518 0.414 0.438 0.484
DDPM 0.200 0.300 0.433 0.542 0.520 0.469 0.496 Tropentarn 0.178 0.500 0.575 0.445 0.292 0.329 0.367
It is seen that acquired spectra has no peaks and that the slopes are not steep, except of probably that of 850 nm. So there is no necessity to measure the whole spectrum in further experiments and is enough just to track the data at the xed wavelengths.
4.2.2 In the articial environment
The similar measurements, performed for monochromic and patterned spectra, but at the hot and humid articial environment gave us the following data. Tables 9 and 10 shows us the results for monochromic and patterned samples accordingly.
Table 9: Monochromic samples in the articial environment.
λ, nm 750 850 950 1050 1150 1250 1350 Fleece 0.292 0.320 0.360 0.226 0.182 0.236 0.171 CP 0.164 0.261 0.599 0.495 0.426 0.370 0.187 Nylon 0.223 0.333 0.314 0.362 0.340 0.300 0.212 Table 10: Patterned samples in the articial environment.
λ, nm 750 850 950 1050 1150 1250 1350 ACUPAT 0.219 0.325 0.547 0.367 0.294 0.243 0.209
DDPM 0.207 0.358 0.415 0.508 0.403 0.358 0.196 Tropentarn 0.168 0.601 0.574 0.349 0.213 0.193 0.118
Before statistical processing let us take into account the absorption of the water vapour. For this purpose we have to divide the charateristic numbers of the samples onto the appropriate numbers of water absorption spectrum. The spectrum was found in part 2.2. Table 11 shows us the estimated absorption in the vaporous gap with given diameter.
Meanwhile, this estimation is very rough due to uncontrolled vapour temperature and unknown vapour spread distance. This estimation only helps us to clear the data more or less. Tables 12 and 13 shows us the results for monochromic and patterned samples accordingly.
Now let us calculate the correlation coecients between the data, acquired from the measurements in the pure environment, and the measurements in the articial humid environment. Let us also calculate cross-correlation coecients, i.e.
corre-Table 11: Absorption in the vaporous gap.
Table 12: Monochromic samples in the articial environment w/o vapour.
λ, nm 750 850 950 1050 1150 1250 1350 Fleece 0.296 0.326 0.418 0.241 0.250 0.349 0.484 CP 0.166 0.266 0.695 0.527 0.587 0.547 0.530 Nylon 0.226 0.340 0.364 0.386 0.468 0.445 0.600 Table 13: Patterned samples in the articial environment w/o vapour.
λ, nm 750 850 950 1050 1150 1250 1350 ACUPAT 0.222 0.331 0.594 0.451 0.405 0.359 0.503
DDPM 0.209 0.365 0.481 0.541 0.555 0.530 0.554 Tropentarn 0.169 0.613 0.666 0.371 0.293 0.286 0.334
lation between dierent materials. Both types of coecients are used in order to prove or to reject the equivalence hypotheses. Table 14 shows us these coecients.
Cross-correlation is high enough sometimes, as in the case of patterned materials we have similar spectra.
Using the data from part 3.7 it can be shown, whether the samples can be clearly distinguished one from another or not. Table 15 claries the situation.
Table 14: Correlation between samples.
Sample True at 95% True at 99% Can be missed? (99%)
Fleece yes no no
With all the assumptions done the samples can be distinguished one from each other with at least 95% probability. For cotton-polyester samples this probability can be increased up to 99%. One should also take into account that in the calcu-lations above we have used water vapour absorption approximation, which could be not precise for the conditions used. Anyway, the quality of distinguishing can be increased either by increasing the number of channels, or by some intelligent selection of sensing wavelengths.
5 CONCLUSION
The objective of this work was to try to nd out the possibility of creation an optical target recognition system, using target aimed scanning at several xed wavelengths. It was desired to use near infrared light as the most convenient from all of the points of view. Materials and patterns were selected in a way to represent better some modern military gear and equipment.
The series of conducted experiments and the data that was obtained, showed us that the proposed algorithm can be used for the purposes declared. In other words one can take a sample, measure it, process, compare to all the references, stored in the database, and denitely say, which one was measured. Of course, in the case when the samples are very similar, this algorithm will not work. But in our case it was not required to distinguish similar targets as dierent ones.
Many factors had not been taken into consideration during the experiments. In a real life situation the atmosphere may dier a lot from those two that were used while testing. There may be dust. Unfortunately it was not possible to simulate dusty air conditions, as this would have required special equipment. There still re-mains a problem of proper calibration of such a system. Within the bounds of this work the simplest calibration method was proposed. It will be dicult to calibrate this way in non-laboratory conditions. No electronic part was considered, although it can bring in additional errors into the incoming data too.
The overall price of such a system, if we will take into consideration only optical and electronic components, can be even less than 1000 euros. Unfortunately, we can not compare this price, as the prices of the systems analogous to this are un-known.
This work may be helpful for those, who conducts investigations in the area of ATR or in the area of distant optical measurements. Also it may be tried to assemble the proposed system for optimisation and further research. Denitely, those ones will be in demand in future.
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