Wegener (1990) showed a method in which the probability density function for each detector is calculated and stored in a look-up table. The ‘Histogram statistics’ method is another form of relative gain estimate in which on-board calibrators and imaging maneuvers are not needed. The relative gains from the diffuser method for Landsat 8 are stored in a calibration parameter file (CPF) available for public use. The diffuser panel may also degrade to the point where electronic noise and signal noise may dominate the measurements by the detectors, creating a decrease in relative gain quality. Specifically, detectors located within the end FPMs, FPM 1 and 14 for the OLI bands, tend to receive less light than those in the middle, creating negative impacts on the relative FPM and detector gains. Unfortunately, this method tends to vary over time as the solar diffuser panel acquires impurities that affect accuracy of the calibration. It is these differences that are the basis behind the relative gain calculations. With an ideal diffuser panel and ideal detectors, the diffuser panel image would be a uniform image however, there are detector-level differences across the diffuser image. This is the current method used by Landsat 8, where a solar diffuser panel deployed in front of the aperture of the OLI reflects light from the sun and scatters the intensity evenly across the array of detectors so that each detector views the same high-intensity amount of light. Knight and Kvaran (2014) explored a method where a solar diffuser panel was used to illuminate all detectors within Landsat 8. There are multiple ways to remotely calibrate detectors using uniform bright light. The organization of this paper is as follows: Introduction, Background, Methodology, followed by Results and Discussion, and ending with the Conclusion. This paper illustrates the efficacy of using the yaw maneuver, also known as the side-slither (SS) technique, as defined in Section 3.1, to derive relative gains between detectors and across an entire detector array. With recently launched satellites having more spectral bands than their previous counterparts, and therefore many more detectors to calibrate, a useful and efficient calibration method is needed to remove the nonuniformities across detectors in each spectral band. However, the multispectral data acquired by the sensors tend to be influenced by multiple factors leading to nonuniformities including atmospheric scattering and absorption, differences in sensor manufacturing, electrical noise, and differences in each detector’s gains and linear responses, which change over time. Radiometric calibration is the process of converting DN values into physical units, such as reflectance, for analysis. Normally, earth-imaging sensors, after detection, amplification, and analog-to-digital conversion, convert the level of electromagnetic radiation at the aperture into a digital number (DN) that has no units. Multispectral Earth data, i.e., electromagnetic radiation, is acquired through the use of imaging sensors onboard earth-imaging satellites. While Landsat 8 is used as an example, the methodology is applicable to all linear array sensors that can perform a 90 ∘ yaw maneuver. Both reflective and thermal wavelengths are corrected to a level that rivals current operational methods. Relative gains derived from the side-slither technique and applied to imagery provide a visual and statistical reduction in detector-level and FPM-level striping and banding in Landsat 8 imagery. A periodic model based on in-scene FPM corrections was designed as a go-to model for all bands aboard Landsat 8. To correct for each detector’s differences in sensor measurement, a novel technique of relative gain estimation that employs an optimized modified signal-to-noise ratio through a 90 ∘ yaw maneuver, also known as side slither, is presented that allows for both FPM and detector-level relative gain calculation. These 73,000 detectors are split among 14 different focal plane modules (FPM), and each detector and FPM exhibit unique behavior when monitoring a uniform radiance value. Landsat 8, a well-known example, uses pushbroom scanning and thus has 73,000 individual detectors. Earth-imaging satellites commonly acquire multispectral imagery using linear array detectors formatted as a pushbroom scanner.
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