Use of hyperspectral and multi-angle CHRIS Proba image for land cover maps generation
2008
In the last decades, many satellite missions have marked the history of the
optical remote sensing, providing a wide choice of products for every kind of
application and investigation. The Proba-1 program can not be compared in
importance and budget to very important missions like Landsat and SPOT,
but the innovations introduced in the payload design have attracted the attention
of principal investigators and scientists since the beginning. The new
spectral configuration of its high resolution sensor CHRIS can represent an
innovation for the wide family of the optical instruments. In fact CHRIS is
one of the experiments, as well as NASA Hyperion, concerning the developing
of the expensive hyperspectral technology for the satellite environment.
Moreover, the innovative solutions for the autonomous orbit maintaining and
navigation, permit to the spacecraft difficult manoeuvres and multi-angular
acquisitions during the target overpass. The result is an extraordinary and
unique database of hyperspectral and multi-directional images acquired several
times during the years over predefined test sites.
This research work proposes a complete treatment of the CHRIS products
since the radiometric and geometric correction till the classification processing.
Phases like destriping, atmospheric corrections, spectral adjacency compensation
and ortho-rectification have been performed and developed. Several
classification exercises have been proposed with the aim of evaluating the impact of the principal key factors of the Proba mission (hyperspectral,
multi-angular and multi-temporal) to the final classification accuracy. The
directional anisotropy of the refiectance, as well as its temporal dependence,
has been well explained and investigated trough the use of spectral models
and radiative equations, but studies and applications based on real satellite
high resolution and multi-directional data over lands and water bodies are
few or still ongoing. Said that, this research work has been addressed to
asses if these additional information, which reflect additional costs in terms
of satellite technology and image processing are justified with significant improvements
for the land cover production, as one of the most diffused application
in the research environment.
Once again the neural networks have confirmed their effectiveness for the
classification of optical images at high and very high resolution. The new
spectral, multi-angular and multi-temporal inputs have been well managed
and used as additive information for the decision task, without impacting
the design of the classification scheme. On the whole the results have been
satisfactory in most cases.
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