Lookup Table Approach for Radiometric Calibration of Miniaturized Multispectral Camera Mounted on an Unmanned Aerial Vehicle

Abstract

Over recent years, miniaturized multispectral cameras mounted on an unmanned aerial vehicle (UAV) have been widely used in remote sensing. Most of these cameras are integrated with low-cost, image-frame complementary metal-oxide semiconductor (CMOS) sensors. Compared to the typical charged coupled device (CCD) sensors or linear array sensors, consumer-grade CMOS sensors have the disadvantages of low responsivity, higher noise, and non-uniformity of pixels, which make it difficult to accurately detect optical radiation. Therefore, comprehensive radiometric calibration is crucial for quantitative remote sensing and comparison of temporal data using such sensors. In this study, we examine three procedures of radiometric calibration: relative radiometric calibration, normalization, and absolute radiometric calibration. The complex features of dark current noise, vignetting effect, and non-uniformity of detector response are analyzed. Further, appropriate procedures are used to derive the lookup table (LUT) of correction factors for these features. Subsequently, an absolute calibration coefficient based on an empirical model is used to convert the digital number (DN) of images to radiance unit. Due to the radiometric calibration, the DNs of targets observed in the image are more consistent than before calibration. Compared to the method provided by the manufacturer of the sensor, LUTs facilitate much better radiometric calibration. The root mean square error (RMSE) of measured reflectance in each band (475, 560, 668, 717, and 840 nm) are 2.30%, 2.87%, 3.66%, 3.98%, and 4.70% respectively.

Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

“…Due to sensor degradation, these values are likely to gradually decline over time, lessening the accuracy of the radiometric calibration processing (Mamaghani and Savaggio, 2019). Studies have improved sensor performance by performing vicarious radiometric calibration using ground targets and panels with known radiometric accuracy, calibrating sensors using National Institute of Standards (NIST)-traceable equipment in a laboratory, and developing look-up tables for correction factors to update calibration parameters (Del Pozo et al, 2014;Mamaghani and Salvaggio, 2019;Cao et al, 2020). Baek et al (2020) conducted an assessment on radiometric accuracy for the MicaSense RedEdge-MX sensor by comparing data to hyperspectral sensors with NIST-traceable calibration (TriOS RAMSES) and showed that MicaSense RedEdge-MX radiance is approximately 5-16% lower, and irradiance is approximately 1-20% lower, depending on wavelength (Baek et al, 2020).…”

Case Study on the Fitting Method of Typical Objects

“…However, the spectrometer's accuracy will be affected by a variety of error sources. The main error sources are the stray light error [19][20][21] and the detector non-uniformity error [22][23][24]. These errors make the equation AX = B unsuitable and add difficulty obtaining a solution for the equation.…”

Vignetting Correction Based on a Two-Dimensional Gaussian Filter With Harmony for Area Array Sensors

“…The LUT method provides a correction factor for each pixel and has the highest accuracy for vignetting correction. It is a common method to generate the LUT for vignetting correction, where reference images obtained under uniform illumination are used to extract the vignetting background [7,16]. Subsequently, the vignetting background is used to generate the LUT, which is typically the ratio of the maximum value of the reference image to the value of each pixel, given the impact of image noise.…”