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”.
Bitelli, G., Blanos, R., Conte, P., Mandanici, E., Paganini, P., Pietrapertosa, C. (2017). Hyperspectral Data Classification to Support the Radiometric Correction of Thermal Imagery. Cham : Springer International Publishing [10.1007/978-3-319-62401-3_7].
Hyperspectral Data Classification to Support the Radiometric Correction of Thermal Imagery
BITELLI, GABRIELE;CONTE, PAOLO;MANDANICI, EMANUELE;
2017
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”.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.