Abstract
Ground control points (GCPs) are critical for agricultural remote sensing that require georeferencing and calibration of images collected from an unmanned aerial vehicles (UAV) at different times. However, the conventional stationary GCPs are time-consuming and labor-intensive to measure, distribute, and collect their information in a large field setup. An autonomous mobile GCP and a collaboration strategy to communicate with the UAV were developed to improve the efficiency and accuracy of the UAV-based data collection process. Prior to actual field testing, preliminary tests were conducted using the system to show the capability of automatic path tracking by reducing the root mean square error (RMSE) for lateral deviation from 34.3 cm to 15.6 cm based on the proposed look-ahead tracking method. The tests also indicated the feasibility of moving reflectance reference panels successively along all the waypoints without having detrimental effects on pixel values in the mosaicked images, with the percentage errors in digital number values ranging from −1.1% to 0.1%. In the actual field testing, the autonomous mobile GCP was able to successfully cooperate with the UAV in real-time without any interruption, showing superior performances for georeferencing, radiometric calibration, height calibration, and temperature calibration, compared to the conventional calibration method that has stationary GCPs.
Highlights
The proposed method performed better in terms of radiometric calibration, with error in calibration ranging from 0.115% to 2.771%, as compared to 1.364–6.556% for the conventional method. These results indicate that radiometric calibration based on the mobile Ground control points (GCPs) can more efficiently account for variation in illumination and atmospheric conditions on unmanned aerial vehicles (UAV) images taken across the field, compared to those obtained with only one set of calibration panels
An autonomous mobile GCP was developed for the purposes of improving UAV-based agricultural data collection efficiency and georeferencing, radiometric, height, and temperature calibration accuracies
A navigation system was developed for automatic path tracking and wireless communication between the autonomous GCP and UAV for collaboration in field operations
Summary
The use of unmanned aerial vehicles (UAVs) as platforms to collect high-resolution multi-spectral imagery is increasing rapidly in a wide variety of environmental and geographical studies including in agriculture [1,2,3], forestry [4,5,6], ecology [7,8,9], mining [10,11,12], coastal assessments [13,14,15], and fluvial surveys [16,17,18]. Employing high-precision GCPs in field survey measurements is generally critical to improve the geometric accuracy and quality of digital terrain models (DTMs), DSMs, and image mosaics [33,34]. Some studies [35,36,37] have demonstrated that increasing the quantity of GCPs installed across the fields, especially when the area of interest is large, can remarkably improve photogrammetric surveys
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