Luminance calibration of a full sky HDR imaging system using sky scanner measurements

A full sky High Dynamic Range imaging system, based on a Single-Lens Reflex camera with a fisheye lens, has been constructed and calibrated with a sky scanner luminance meter. The method considers the geometrical, spectral, timing and orientation issues between instruments. The calibration data sets...

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Detalles Bibliográficos
Autores: García Ruiz, Ignacio, Sáenz Gamasa, Carlos, Hernández Salueña, Begoña, García Santos, Rafael, Torres Escribano, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/43345
Acceso en línea:https://hdl.handle.net/2454/43345
Access Level:acceso abierto
Palabra clave:HDR imaging
Sky luminance
Sky scanner
Sky imager
Luminance calibration
Descripción
Sumario:A full sky High Dynamic Range imaging system, based on a Single-Lens Reflex camera with a fisheye lens, has been constructed and calibrated with a sky scanner luminance meter. The method considers the geometrical, spectral, timing and orientation issues between instruments. The calibration data sets, having nearly simultaneous measurements under stable sky conditions, were obtained from approximately one month of data using selection variables based in the experimental design. For luminance estimation we use the standard RGB combination and a Spectrally Matched Luminance () predictor, matching the spectral response of the instruments. With 738 calibration points having luminances up to 23.6 kcd∕m2, covering 98.5% of the sky luminance range, is linearly correlated with sky scanner measurements with a coefficient of determination 2 = 0.9927 and a Root Mean Squared Error () of 7.7%. gives better results, with 2 = 0.9973 and = 5.3%. With 253 calibration points with luminances up to 12.9 kcd∕m2, comprising 94.1% of the sky luminance range, both predictors clearly improve, with 2 = 0.9964 and = 4.1% in case of and 2 = 0.9982 and = 2.9% in case of .