<title>Methods for CCD camera characterization</title>

Abstract

In this paper we present methods for characterizing CCD cameras. Interesting properties are linearity of photometric response, signal-to-noise ratio (SNR), sensitivity, dark current and spatial frequency response (SFR). The techniques to characterize CCD cameras are carefully designed to assist one in selecting a camera to solve a certain problem. The methods described were applied to a variety of cameras: an Astromed TE3/A with P86000 chip, a Photometrics CC200 series with Thompson chip TH7882, a Photometrics CC200 series with Kodak chip KAF1400, a Xillix' Micro Imager 1400 with Kodak chip KAF1400, an HCS MXR CCD with a Philips chip and a Sony XC-77RRCE.

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

“…Not every photon, however, generates a charge. Quantum efficiency defines the proportion of incoming photons capable of liberating electrons through the photoelectric effect [28]. The quantum efficiency of sensors varies both between materials and across wavelengths, therefore altering the amount of incoming radiance required to generate a proportionate charge between differing bandpass filters.…”

“…Through the physical isolation of the sensor from incoming radiance, the radiance component can be globally reduced to zero. Dark offset imagery is raw image data generated such that it contains only the noise component [26,28]. Each dark offset image represents a single sample of the per-pixel noise within the sensor.…”

Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography

“…Raw DNs collected through the charge-coupled device (CCD) or the complementary metal oxide semiconductor (CMOS) sensor have a linear response to light intensity (Mullikin et al, 1994;Cescatti, 2007;Lang et al, 2010). In contrast, DNs in JPEG format have a non-linear relationship to light intensity that results from the application of automatic gamma corrections, as well as other in-camera processing (Cescatti, 2007).…”

A model for dark current characterization and simulation

“…The constant dark current background associated with diffusion current and the fixed row and column patterns that are created by imperfect referencing and by column processing circuits are not considered. Dark current is usually described only by its second order statistics, and frequently just by its mean value 5,6 . Such simple descriptions are misleading because they contain the implicit presumption that the probability distribution is Gaussian.…”