基于无人机遥感的玉米株高提取方法

In order to accurately and quickly grasp the growth information of maize in the growth cycle, different digital orthophoto maps（DOM）and digital surface model （DSM） in the four stages of the nutritional growth stage of maize were obtained by unmanned aerial vehicle（UAV）. K-means, genetic neural network and skeleton algorithm were used to extract the maize areas in the DOM, generate masks, and combined with DSM sets to obtain the height information of maize. Compared with the field measurement of plant height, the R2 of three methods were 0.853, 0.877 and 0.923, respectively, RMSE were 15.886cm, 14.519cm and 11.493cm, respectively, MAE were 13.743cm, 11.884cm and 8.927cm, respectively. The results showed that combining DOM and DSM can better extract the height value of maize in the nutritional growth stage. Compared with K-means and genetic neural network, the maize height extracted by the skeleton algorithm was highly consistent with the field measurement (R2 was 0.923, RMSE was 11.493cm, MAE was 8.927cm), and the extraction accuracy was high. Skeleton extraction combining DOM and DSM provided a way to extract plant height, which can be used as a reference for monitoring maize height by UAV remote sensing.