RT Journal Article
T1 Standardized spectral and radiometric calibration of consumer cameras
A1 Burggraaff, Oliver
A1 Schmidt, Norbert
A1 Zamorano Calvo, Jaime
A1 Pauly, Klaas
A1 Pascual Ramírez, Sergio
A1 Tapia Ayuga, Carlos
A1 Spyrakos, Evangelos
A1 Snik, Frans
AB Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database.
PB The Optical Society Of America
SN 1094-4087
YR 2019
FD 2019
LK https://hdl.handle.net/20.500.14352/13758
UL https://hdl.handle.net/20.500.14352/13758
LA eng
NO © 2019 Optical Society of America. Funding by Horizon 2020 Framework Programme (grant nr. 776480, MONOCLE and grant nr. 824603, ACTION). The authors wish to thank Molly and Chris MacLellan of the NERC Field Spectroscopy Facility for experimental help and invaluable insights in the flat-field and spectral response measurements. Figure 1 was drawn using draw.io. Data analysis and visualization were done using the AstroPy, ExifRead, Matplotlib, NumPy, RawPy, and SciPy libraries for Python. Finally, the authors wish to thank the two anonymous reviewers for their thorough and constructive reviews. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776480.
NO Unión Europea. H2020
DS Docta Complutense
RD 28 abr 2025