Error Compensation Algorithm for UAV Telemetry Temperature Data

In recent years, the UAV industry has developed rapidly, and UAVs have been applied to many fields such as surveying, agriculture, military and even home entertainment. At present, my country has carried out drone remote sensing temperature measurement experiments in many experimental fields. In the experiment, drones equipped with infrared thermal imaging cameras were used to scan and measure the jointing stage corn of different traits. At the same time, portable handheld thermometers were used to measure Simultaneously measure the corn canopy temperature on the ground, and conduct a consistency analysis with the extracted canopy temperature to verify and evaluate the effect of extracting the corn canopy temperature based on thermal infrared images. Based on the corn canopy test data in this field, this paper mainly studies and solves the problems encountered in the temperature measurement accuracy in the post-processing of the test data, and proposes a temperature compensation method for UAV remote sensing temperature data based on wavelet neural network. It is a good solution to the problem that the accuracy cannot meet the requirements during the measurement process. It can be used in the data post-processing of remote sensing temperature measurement test of drones, so that the obtained data results are more accurate, and the measurement accuracy can also be greatly improved.