Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy.
Sensors (Basel). 2022 Aug 29;22(17):6490. doi: 10.3390/s22176490.
UAVs are sensor platforms increasingly used in precision agriculture, especially for crop and environmental monitoring using photogrammetry. In this work, light drone flights were performed on three consecutive days (with different weather conditions) on an experimental agricultural field to evaluate the photogrammetric performances due to colour calibration. Thirty random reconstructions from the three days and six different areas of the field were performed. The results showed that calibrated orthophotos appeared greener and brighter than the uncalibrated ones, better representing the actual colours of the scene. Parameter reporting errors were always lower in the calibrated reconstructions and the other quantitative parameters were always lower in the non-calibrated ones, in particular, significant differences were observed in the percentage of camera stations on the total number of images and the reprojection error. The results obtained showed that it is possible to obtain better orthophotos, by means of a calibration algorithm, to rectify the atmospheric conditions that affect the image obtained. This proposed colour calibration protocol could be useful when integrated into robotic platforms and sensors for the exploration and monitoring of different environments.
无人机是在精准农业中越来越多使用的传感器平台，特别是用于使用摄影测量学进行作物和环境监测。在这项工作中，在一个实验性农田上进行了三次连续的轻无人机飞行（具有不同的天气条件），以评估由于色彩校准而产生的摄影测量性能。从三天和场地的六个不同区域中进行了三十次随机重建。结果表明，经过校准的正射影像比未经校准的影像显得更绿更亮，更好地代表了场景的实际颜色。校准重建中的参数报告错误总是较低，而未校准重建中的其他定量参数总是较低，特别是在相机站总数和重投影误差方面观察到显著差异。所得结果表明，可以通过校准算法获得更好的正射影像，从而纠正影响所获得图像的大气条件。当将该色彩校准协议集成到用于探索和监测不同环境的机器人平台和传感器中时，该协议可能会很有用。
