Common noises include high level coarse errors and low level coarse errors. As shown below, High-level coarse error is usually caused by the return of high-flying objects (such as birds or airplanes) during data collection; Low-level coarse error is reflected signals with extremely low orientations caused by the multipath effect of the laser pulse. The Emission Removal tool aims to eliminate these errors as much as possible and hence improve data quality.
First, the algorithm will search for the neighboring points of each point within the user defined area and calculate the average distance from the point to the neighboring points. Then the mean value and standard deviation of these mean distances for all points are calculated. If the average distance from a point to its neighbors is greater than the maximum distance (maximum distance = mean + n * standard deviation, where n is a user-defined multiple), it will be considered an outlier and will be removed from the original point cloud.
Settings Input Data: The input file can be a single point cloud data file or multiple data files. File Format:*. LiDate. Neighborhood points (default value is "10"): The number of points needed in the neighborhood to calculate the average distance to each point. If not enough points are found, the algorithm will fail. Multiples of standard deviation (default value "5"): The factor multiplied by the standard deviation to calculate the maximum distance. Output path: The path to the output file. After the function is executed, a new file will be generated. If more than one file is input, the folder path must be specified. Note: The algorithm of this function can be executed multiple times to improve the noise reduction results. Emission removal results are limited if the noise is too dense.