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”.

Hyperspectral Data Classification to Support the Radiometric Correction of Thermal Imagery

Blanos R;Paganini P;
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”.
978-3-319-62401-3
File in questo prodotto:
File Dimensione Formato  
Bitelli_et_al_ICCSA2017_postprint.pdf

non disponibili

Tipologia: Altro materiale allegato
Licenza: Non specificato
Dimensione 1.43 MB
Formato Adobe PDF
1.43 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.14083/5544
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact