## DESCRIPTION

v.outlier removes outliers in a 3D point cloud. By default, the outlier identification is done by a bicubic spline interpolation of the observation with a high regularization parameter and a low resolution in south-north and east-west directions. Those points that differ in an absolute value more than the given threshold from a fixed value, reckoned from its surroundings by the interpolation, are considered as an outlier, and hence are removed.

The filter option specifies if all outliers will be removed (default), or only positive or only negative outliers. Filtering out only positive outliers can be useful to filter out vegetation returns (e.g. from forest canopies) from LIDAR point clouds, in order to extract Digital Terrain Models. Filtering out only negative outliers can be useful to estimate vegetation height.

There is a flag to create a vector that can be visualizated by qgis. That means that topology is build and the z coordinate is considered as a category.

## EXAMPLES

### Basic outlier removal

    v.outlier input=vector_map output=vector_output outlier=vector_outlier thres_O=25

In this case, a basic outlier removal is done with a threshold of 25 m.

### Basic outlier removal

    v.outlier input=vector_map output=vector_output outlier=vector_outlier qgis=vector_qgis

Now, the outlier removal uses the default threshold and there is also an output vector available for visualizaton in QGIS (http://www.qgis.org).

### North carolina location example

    v.outlier input=elev_lid792_bepts output=elev_lid792_bepts_nooutliers \
      outlier=elev_lid792_bepts_outliers ew_step=5 ns_step=5 thres_o=0.1

## NOTES

This module is designed to work with LIDAR data, so not topology is built but in the QGIS output.

## SEE ALSO

v.surf.bspline

## AUTHORS

Original version of the program in GRASS 5.4:
Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni and Mirko Reguzzoni
Updates for GRASS 6:
Roberto Antolin