International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
A comparison between the classifications indicated that they
were significantly different. A Z statistic of 3.7 (Hyperion
and RDACS-3) and a Z statistic of 4.5 ((Hyperion and
AVIRIS) were computed for the pairwise comparisons.
Because the Z statistic values were greater than 1.96, there
were significant differences in the results of the classifications
of the sensors.
Rufledanse
X
==
Mange TEE
Figure 9. Spectra of black willow; healthy and under stress
(the scale is between 0 and 1 (100%) for the reflectance axis)
6. CONCLUSIONS
Airborne and spaceborne hyperspectral imagery is becoming
increasingly accessible due to the increasing number of
companies and agencies operating hyperspectral scanners.
Airborne data acquisitions benefit greatly over satellite based
missions because the user has influence on the mission in
terms of time schedules, flight line arrangements, calibration
measurements, spectral/spatial resolutions, and acceptable
weather conditions. However, airborne hyperspectral
Figure 7. Classification map of the Hyperion dataset
sensors are often very expensive due to fact that limited
spatial coverage and multiple flight lines may be required to
cover a study area. Also, data processing is usually complex
and can cause problems.
Another objective of this research was to outline forested
areas that were under stress due to a drought. Figure 9 shows
the spectra of black willow leaves; healthy (cyan, green and
red lines), moderately stressed (blue) and severely stressed
(black). Another objective was to map the water quality in
the lakes using the hyperspectral data (Figure 10). However,
because of the page limitations, these studies were not
Airborne hyperspectral sensors are usually used to test
spaceborne hyperspectral sensors, which provide continuous
MR coverage of most of our planet as well planetary surfaces.
included in this paper.
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