Relative Radiometric Calibration Using Tie Points and Optimal Path Selection for UAV Images

Abstract

As the use of unmanned aerial vehicle (UAV) images rapidly increases so does the need for precise radiometric calibration. For UAV images, relative radiometric calibration is required in addition to the traditional vicarious radiometric calibration due to the small field of view. For relative radiometric calibration, some UAVs install irradiance sensors, but most do not. For UAVs without them, an intelligent scheme for relative radiometric calibration must be applied. In this study, a relative radiometric calibration method is proposed to improve the quality of a reflectance map without irradiance measurements. The proposed method, termed relative calibration by the optimal path (RCOP), uses tie points acquired during geometric calibration to define the optimal paths. A calibrated image from RCOP was compared to validation data calibrated with irradiance measurements. As a result, the RCOP method produces seamless mosaicked images with uniform brightness and reflectance patterns. Therefore, the proposed method can be used as a precise relative radiometric calibration method for UAV images.

Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

“…The first product in SFM workflow are the tie points, so tie points are compared for 16 projects. Tie points refer to image points from different images which correspond to the same ground points [51]. The number of tie points has a significant influence on triangulation process and accuracy of resultant DSM.…”

Assessment of DSM Based on Radiometric Transformation of UAV Data

“…These are the points in image space corresponding to the similar ground points [ 40 ]. In tie points generation, the surface texture is the most important factor.…”

Early infestations by arthropod pests induce unique changes in plant compositional traits and leaf reflectance

“…However, applied and fundamental research into hyperspectral remote‐sensing technologies may lead to the construction of simpler sensors to acquire signals in a few spectral bands in which crop leaf reflectance values have been identified as strong and reliable indicators of crop stress. For hyperspectral remote‐sensing technologies to become widely adopted, it is equally important to mention challenges related to the calibration of sensor hardware (signal‐to‐noise ratio of data collected with different systems) and spectral data calibration (for comparison of data collected at different time points and under different abiotic conditions) 15,17,22–26 . Inconsistencies and stochastic noise in the calibration of both sensor hardware and spectral data adversely affect the performance (accuracy and robustness) of classification algorithms, so studies are needed in which the overall sensitivity of classification algorithms to stochastic reflectance noise is quantified experimentally 27 .…”

“…For hyperspectral remote‐sensing technologies to become widely adopted, it is equally important to mention challenges related to the calibration of sensor hardware (signal‐to‐noise ratio of data collected with different systems) and spectral data calibration (for comparison of data collected at different time points and under different abiotic conditions). 15 , 17 , 22 , 23 , 24 , 25 , 26 Inconsistencies and stochastic noise in the calibration of both sensor hardware and spectral data adversely affect the performance (accuracy and robustness) of classification algorithms, so studies are needed in which the overall sensitivity of classification algorithms to stochastic reflectance noise is quantified experimentally. 27 In addition, it is important to carefully examine underlying associations between leaf reflectance and plant physiology/metabolism as ways to potentially describe likely effects of biotic stress, and this was investigated in this study.…”