Abstract
This study presents a complex empirical image-based radiometric calibration method for a Tetracam μMCA multispectral frame camera. The workflow is based on a laboratory investigation of the camera’s radiometric properties combined with vicarious atmospheric correction using an empirical line. The effect of the correction is demonstrated on out-of-laboratory field campaign data. The dark signal noise behaviour was investigated based on the exposure time and ambient temperature. The vignette effect coupled with nonuniform quantum efficiency was studied with respect to changing exposure times and illuminations to simulate field campaign conditions. The efficiency of the proposed correction workflow was validated by comparing the reflectance values that were extracted from a fully corrected image and the raw data of the reference spectroscopy measurement using three control targets. The Normalized Root Mean Square Errors (NRMSE) of all separate bands ranged from 0.24 to 2.10%, resulting in a significant improvement of the NRMSE compared to the raw data. The results of a field experiment demonstrated that the proposed correction workflow significantly improves the quality of multispectral imagery. The workflow was designed to be applicable to the out-of-laboratory conditions of UAV imaging campaigns in variable natural conditions and other types of multiarray imaging systems.
Highlights
Unmanned Aerial Vehicles (UAVs) equipped with miniature multispectral sensors are popular spectral imaging tools for nondestructive environmental monitoring in many fields of study, such as precision agriculture, forestry and nature protection [1,2,3,4,5]
We have proposed and tested a reliable calibration workflow enabling straightforward empirical, image-based radiometric corrections combined with vicarious atmospheric correction using the empirical line approach
This study proposed and tested a complex empirical image-based radiometric calibration and atmospheric correction method for a Tetracam μMCA multispectral frame camera with a global shutter
Summary
Unmanned Aerial Vehicles (UAVs) equipped with miniature multispectral sensors are popular spectral imaging tools for nondestructive environmental monitoring in many fields of study, such as precision agriculture, forestry and nature protection [1,2,3,4,5]. Compared to spaceborne multispectral sensors, UAV sensors feature substantially higher spatial resolutions, temporal resolution, especially in areas with frequent cloud cover, and greater flexibility when designing the imaging campaigns and selecting suitable payloads [6]. UAV sensors have lower operational costs and better spatial resolutions, enabling more detailed analyses of study sites [7]. The key role of UAV imaging is to conduct detailed mapping of spatially highly heterogeneous or dynamic phenomena in often varying natural conditions. The ability to acquire correct spectral imaging results that are unbiased by the sensor properties or the atmospheric or topographic conditions is vital for the reliability and reproducibility of the results
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