A method suitable for cross calibration of UAV hyperspectral remote sensors

High-precision radiometric calibration is the basis for quantitative applications of hyperspectral remote sensing. Cross-calibration facilitates the cross-comparison and radiation reference transfer between multi-source hyperspectral equipment and normalizes different remote sensors to a common radiometric baseline. In the collaborative use of different unmanned aerial vehicle (UAV) hyperspectral observations, cross-calibration helps to eliminate the differences in the radiometric and spectral scales of the multi-source remote sensors, improve the radiometric quality and interpretation consistency of the imaging from different remote sensors. However, a significant portion of the error in cross-calibration between UAV hyperspectral instruments using radiation transfer modeling comes from the assumption of aerosol type. When using the irradiance method for calculations, it is important to consider the case that the uplink radiation transfer from the UAV remote sensors passes through only a portion of the atmosphere. Therefore, cross-calibration is necessary to improve the radiation transfer model with its own characteristics. In this paper, we propose the cross-calibration method for UAV hyperspectral to address the above problems. A full set of data such as multi-gray level target images, atmospheric aerosol, water vapor content data, etc. are collected in our experiment. The method improves the traditional irradiance calibration method by combining the measured atmospheric diffuse-to-global ratio, and effectively reduces the error caused by the aerosol assumption by taking into account the special characteristics of the uplink radiation transmission path of the UAV. At the same time, considering that it is difficult to satisfy the need of cross-calibration of the whole response interval by using a single reflectance feature, the experiment adopts six kinds of targets with different gray levels for cross-calibration. Finally, the accuracy and impact of different response intervals are analyzed. The results demonstrate that the method proposed in this paper can ensure the cross-calibration accuracy more reliably, especially when the aerosol type is difficult to be determined, and it is very suitable for cross-radiometric calibration between UAV sensors.