Abstract
The derivation of surface temperature from thermal images requires a proper modelling of the spectral characteristics of the observed surfaces, in particular emissivity. Several possible approaches have been developed in literature. A first category of methods relies on the availability of multiple bands in the thermal region, while a second family of methods, which can be applied also with a single channel sensor, requires the derivation of emissivity values from ancillary data.
The methodology, discussed in the present paper, involves the use of hyperspectral images acquired by an AISA Eagle 1 K sensor installed on board an aircraft platform. Data are composed of 61 bands in the visible and near-infrared region. A supervised classification approach was adopted to derive a map of the main materials appearing in the scene, with special attention to roofing materials. The presented analyses were performed in a portion of the urban area of Treviso (Italy), where two aerial surveys, one with a thermal sensor and the second with the AISA sensor, were carried out in 2011.
All the presented activities were conducted in the framework of the European project “EnergyCity - Reducing energy consumption and CO2 emissions in cities across Central Europe”.
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Acknowledgements
The work was primarily performed in the framework of Central Europe project 2CE126P3 “EnergyCity - Reducing energy consumption and CO2 emissions in cities across Central Europe” (PI T. Csoknyai).
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Bitelli, G., Blanos, R., Conte, P., Mandanici, E., Paganini, P., Pietrapertosa, C. (2017). Hyperspectral Data Classification to Support the Radiometric Correction of Thermal Imagery. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_7
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