Radiometric calibration of a large-array commodity CMOS multispectral camera for UAV-borne remote sensing

• Published in: International journal of applied earth observation and geoinformation 2022-08, Vol.112, p.102968, Article 102968
• Main Authors: Zhou, Xiaoteng, Liu, Chun, Xue, Yun, Akbar, Akram, Jia, Shoujun, Zhou, Yuan, Zeng, Doudou
• Format: Article
• Language: English
• Subjects: Atmospheric correction; calibration; cameras; Large-array commodity CMOS; linear models; Multispectral camera; nonlinear models; Radiometric calibration; radiometry; reflectance; semiconductors; spatial data; Unmanned aerial vehicle (UAV); unmanned aerial vehicles
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2022.102968

•An indoor and outdoor integrated radiometric calibration method for large-array commodity CMOS multispectral cameras.•A nonlinear quantum efficiency calibration model for poor linearity sensors.•A dark target-based empirical line method for low altitude atmospheric correction.•Compared with fitting methods, the lookup table method is a better vignetting effect correction method. To meet the requirement of high-resolution and high-efficiency unmanned aerial vehicle (UAV)-borne multispectral remote sensing, using the miniaturized large-array commodity complementary metal-oxide semiconductor (CMOS) camera is an effective solution. Given the characteristics of the new sensor and platform, almost no systematic and feasible radiometric calibration method has been specifically developed. In this paper, we proposed an indoor and outdoor integrated radiometric calibration method. To develop a systematic indoor calibration method, we explored the optimal methods for dark current offset, vignetting effect correction, and quantum efficiency calibration. According to the comparison results of three different methods, the lookup table (LUT) method was chosen to correct vignetting effect rather than nonlinear regression. Further, we proposed an exponential nonlinear model to replace the traditional linear model for quantum efficiency calibration, which improved the R-squares from around 0.92 to around 0.99. The outdoor calibration included atmospheric path radiance and reflectance correction. We proposed an empirical line method based on the dark target method to correct the atmospheric path radiance before the reflectance correction. Based on our method, the mean absolute percentage errors (MAPE) between the observed reflectance and the true reflectance were around 10%. Moreover, the method can greatly improve the calculated reflectance accuracy of low reflectance targets. Our method can serve as a useful reference for the radiometric calibration of large-array commodity CMOS multispectral cameras. It can also contribute to the application of UAV-borne multispectral remote sensing.