A Ground Point Fitting Method for Winter Wheat Height Estimation Using UAV-Based SfM Point Cloud Data

Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images high-cost LiDAR point cloud, cloud generated by Structure from Motion (SfM) algorithm based on UAV can quickly estimate crop height target area at lower cost. However, leaves gradually start to cover ground beginning stem elongation stage, making more points below canopy disappear data. The terrain undulations outliers will seriously affect estimation accuracy. This paper proposed fitting method winter wheat SfM cloud. A slice filter was designed reduce interference middle outliers. Random Sample Consensus (RANSAC) applied obtain valid filtered Then, missing were fitted according known points. Furthermore, we achieved stage an R2 0.90. relative root mean squared error (RRMSE) 5.9%, absolute (RMAE) 4.6% stage. It concluded that successfully optimized extraction removed Fitting simulated effectively improved