| abstract |
The invention discloses an airborne LiDAR point cloud filtering method based on active learning, comprising steps: S1, acquiring point cloud data and removing low-level noise points; S2, automatically acquiring and marking a training sample set by using multi-scale morphological operations; S3, perform feature extraction on the training sample set and establish an SVM model S4, use the training model to classify the candidate sample set, divide it into a candidate ground point set and a candidate non-ground point set, set the oracle as the candidate point set to the fitting surface The S-type function of the distance, each iteration selects q points from the candidate ground point set and the candidate non-ground point set to add to the training sample set and updates the training model, and iterates until the candidate ground point set and the candidate non-ground point set Until the number of point clouds is no longer greater than q, finally the classification of the latest training model is used as the result of point cloud filtering; S5, filter optimization. The invention can solve the problems that a large number of sample marks are required in the prior art and the filtering precision is not ideal enough. |