Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

Abstract

Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed without precise calibration or with coarse calibration. The radiometric calibration of images from the UAV platform is quite difficult to perform, as the imaging condition is different for every single image. Thus, a standard procedure is necessary for a systematical radiometric calibration method to generate multispectral images with unit reflectance. Further, these images can be used to calculate vegetation indices, which are useful in monitoring vegetation phenology. These vegetation indices are considered as a potential screening tool to know the plant status, such as nitrogen, chlorophyll content, green leaf biomass, etc. This study focuses on a series of radiometric calibrations for multispectral images acquired from different flight altitudes, time instants, and weather conditions. Radiometric calibration for multispectral images is performed using the linear regression method (LRM). The main contribution involves (1) affirming the optimal calibration targets and assessing the atmospheric effects of different flights using the single scene of images; (2) to evaluate the effects of mosaic images with the LRM; (3) to propose and validate a universal calibration equation for the Mini Multiple Camera Array (MCA) 6 camera. The obtained results show that the three calibration targets, such as the dark, moderate, and white, are better for the Mini MCA 6 camera. The atmospheric effects increase with the increase of flight altitudes for each band, and the camera effect is of a fixed number. However, the camera effect and atmospheric attenuation to reflectance from different altitudes were relatively low considering the accuracy assessment. The performance measures namely, mean absolute deviation (indicated as V) and root mean square error (RMSE) between single and mosaic images show that the mosaic will not influence too much reflectance. The LRM performs well in all weather conditions. The universal calibration equation is suitable to apply to the images acquired during a sunny day and even with a little cloud.

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

“…The second feature is particularly significant when periodic monitoring has to be performed, as the operator is free to choose the optimal time to fly. The proposed radiometric calibrations are based on the availability of spectral targets, whose reflectance response is measured in situ with a spectrometer [28][29][30][31][32][33]. After evaluating costs, surveying and processing times, and required instrumentations and expertise, this calibration methodology cannot be adopted as a routine method in precision agriculture.…”

“…As already stressed by many authors in the literature [28][29][30]55], radiometric corrections are necessary when using sensors mounted on UAV for PA, but the ease of use and diffusion is limited. The radiometric processing available in Pix4Dmapper software for the Sequoia camera provides most of the corrections, including vignetting, dark current, exposure time, and sunlight irradiance, but omits other possible causes of radiometric variations [23].…”

Remote sensing data quality model: from data sources to lifecycle phases

“…can be mounted on UAVs to acquire data for mapping, modelling and monitoring spatial phenomena. (Guo et al 2019).…”

Rapid Radiometric Calibration of Multiple Camera Array Using in-Situ Data for Uav Multispectral Photogrammetry

“…If the UAV multispectral camera has a linear relationship between raw DN values and reflectance with intercept close to zero, the empirical line can be constructed using only one light target and zero as dark target. Or a universal empirical equation can be used after performing the corresponding experiment (Guo et al, 2019). However, the universal empirical line is model specific, because of the selected set of filters and cannot be applied to a different set (Assmann et al, 2018).…”

“…Assmann et al (2018) proposed workflow of vegetation monitoring with radiometric calibration using Parrot Sequoia. Iqbal et al (2018) and Guo et al (2019) successfully evaluated the suitability of the empirical line method for atmospheric corrections of UAS data. The common image-based sensor correction methods are a Dark offset subtraction method for noise reduction (Kelcey and Lucieer, 2012b;Mansouri et al, 2005) and Look-Up Table (LUT) method for the vignetting correction (Yu, 2004).…”