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Our research has two major thrusts. The
first involves design of instrumentation to image the earth
and development of methods to extract quantitative information
from remotely sensed data. The second involves development
of methods to generate synthetic images of what the earth
would look like to airborne or satellite imaging systems.
These synthetic image generation tools include models of the
thermal and radiometric behavior of the earth and the atmosphere
which attempt to simulate all of the important factors that
influence the signal level recorded by sensors operating in
the visible through the thermal infrared portions of the spectrum.
On a recent project, as part of our synthetic
image generation work, we acquired data of an instrumented
scene. Calibrated image data were acquired every hour over
a 24-hour period by sensors operating in several spectral
regions. Synthetic representations of the scene were also
produced corresponding to each of the actual images. While
the images matched surprisingly well, it was differences that
were of the most interest. These differences tell us where
either our understanding or modeling of the image-forming
phenomena are flawed. This points us to where we need to study
and improve our models.
The modeling efforts are also valuable
in analyzing actual aerial or satellite images. The quantitative
analysis of these images often involve reverse engineering
using the same modeling concepts used in synthetic image generation.
In particular, the removal of atmospheric effects from remotely
sensed images often involves reversing the same procedures
used in simulating the atmosphere in synthetic images.
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