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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
depend on M and o because they are calculated from the pixels
of the normal concrete.
The anomalies under NS are discriminated as pattern 1 or
pattern 2 in table 5. On the other hand, those under SM and SA
are discriminated as pattern 2 only and those under SB are
mostly discriminated as pattern 3. These patterns don't directly
imply any type of concrete anomalies, however, they can be a
clue for further analysis of the relationship between concrete
anomaly and its temperature distribution.
No pixel was discriminated as the concrete anomaly in line 2
under SA. The pixels like pixel -2 in line lof NS, pixel 2 in line
2 of SB, and pixel -2 in line 3 of SM are judged as anomaly but
are isolated. They might be mixels with vegetation or other land
cover types.
Actually most of the pixels with concrete anomaly are mixels
because the width of a crack or a peeling is within 3cm. The
distribution trend of the temperature and the result of T-test
suggest that airborne thermal remote sensing data is valid for
detecting concrete anomaly.
7. CONCLUSIONS
In this paper, we examined the applicability of airborne thermal
remote sensing data for concrete thermal mapping and anomaly
detection. The shadow effect for the radiant temperature was
measured. The distribution of 4 major land cover types without
anomaly under several shadow conditions showed unique trends.
Statistical T-test was proposed for the detection of concrete
anomaly. The proposed method was capable. Airborne thermal
remote sensing data with 1.5 m is insufficient for the detailed
analysis of concrete anomalies, but the results indicate that there
are great possibilities for detecting the anomaly position.
Future research directions include (1) shadow simulation with
DTM of appropriate resolution to the image, (2) utilization of
multitemporal and high resolution thermal images, (3) data
fusion with optical remote sensing data and ancillary spatial
information, and (4) development of algorithms for the
detection of the concrete anomaly.
59]
ACKNOWLEDGEMENTS
This paper is report 1 from a collaborative research plan
between PASCO Corporation and the Center for the Assessment
and Monitoring of Forest and Environmental Resources
(CAMFER), University of California, Berkeley.
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