The application of Very High Resolution (VHR) satellite imagery to archaeological prospection can furnish useful
information for the identification of archaeological features, related to ancient land use patterns, irrigation networks,
paleo-hydrological systems, roads, walls and buildings. These archaeological features could be enhanced by using data
fusion techniques which are able to merge the complementary characteristics of panchromatic and multispectral images.
The quantitative evaluation of the quality of the fused images is one the most crucial aspects in the context of data
fusion. This issue is particularly relevant in the case of the identification of archaeological features, because data fusion
could enhance or lose the small spatial and spectral details which are generally linked with the presence of buried
archaeological remains.
This study is focused on the evaluation of data fusion algorithms applied to Quickbird images for the enhancement of
archaeological features. Three different data fusion techniques, Gram-Schimdt, PCA, and wavelet, were applied to a
study case located in the South of Italy. Focusing on the archaeological features, the evaluation process was performed
by using two different protocols with and without a reference image. Results obtained from the two protocols showed
that the best performance was obtained from the wavelet data fusion.
A reliable mapping of fuel types is very important for computing fire hazard and risk and simulating fire growth and
intensity across a landscape. Due to the complex nature of fuel characteristic a fuel map is considered one of the most
difficult thematic layers to build up especially for large areas. The advent of satellite sensors with increased spatial
resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate
the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel
properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an
extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel
type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as
ground-truth dataset to assess the results obtained for the considered test area. Results from our analysis showed that the
use ASTER data provided a valuable characterization and mapping of fuel types with a classification accuracy higher
than 78%.
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