LIS Filtering - filtering and classification
This package contains tools for outlier removal from points clouds, point cloud filtering based on attributes, point cloud classification based on grid surfaces, morphological filtering of grid data sets (e.g., DTM generation), and majority filtering of point clouds (e.g., of classification results).
Tool: Attribute Filter
| Features |
|---|
| • filtering of a point cloud based on attribute values |
| • up to three filter attributes, evaluated one after the other |
| • filter methods: drop points, invalidate points, binary flag |
| • filter types: lower limit (min), upper limit (max), min/max, list of invalid values, list of valid values |
| Application |
|---|
| • point cloud filtering / subsetting |
| • point cloud classification |
Tool: Filter Point Cloud with Grid Surface
| Features |
|---|
| • classification of a point cloud based on the elevation difference (offset) to a filter surface provided as grid data set |
| • AOI is defined by grid extent |
| • support of multiple offsets (point cloud slicing) |
| • elevation difference is measured either to grid cell or to triangulation of 3x3 neighborhood |
| • output: either only points matching the filter criteria or all points with additional classification attribute |
| Application |
|---|
| • point cloud classification |
| • point cloud height slicing |
| • point cloud filtering / subsetting |
Tool: Majority Filter (PC)
| Features |
|---|
| • application of a majority filter to a point cloud attribute |
| • majority is calculated from the local neighborhood of each point (2D or 3D) |
| • an inner radius may be specified to transform the shape of the neighborhood to an annulus (a donut) |
| • majority threshold (percent), optional |
| • attribute value range to process, optional |
| • attribute values range to include in majority calculation, optional |
| Application |
|---|
| • classification cleanup / improvement |
| • noise removal |
Tool: Morphological Filter
| Features |
|---|
| • morphological filter operations for grid data sets: erosion, dilation, opening, and closing |
| • square or circular kernel |
| • user defined kernel radius |
| • minimum elevation difference threshold, optional |
| • filling of NoData gaps, optional |
| Application |
|---|
| • removal of small objects |
| • boundary detection |
| • opening or closing of gaps |
Tool: Progressive Morphological Filter
| Features |
|---|
| • removal of non-ground measurements from a grid data set |
| • gradually increasing filter size and elevation difference thresholds |
| • adaptation of the filter proposed by ZHANG, K., CHEN, S.-C., WHITMAN, D., SHYU, M.-L., YAN, J. & C. ZHANG (2003) |
| Application |
|---|
| • DTM generation |
Tool: Remove Duplicate Points (PC)
| Features |
|---|
| • removal of duplicate points from a point cloud by local neighborhood analysis (3D) |
| • several options to decide which point found in the neighborhood should be retained |
| • attribute based selection criterion, optional |
| • removal of all duplicates (including the search point), optional |
| Application |
|---|
| • removal of noise and/or erroneous points |
| • point cloud pre-processing |
Tool: Remove Isolated Points (PC)
| Features |
|---|
| • detection of outliers in a point cloud by local neighborhood analysis (3D) |
| • maximum number of points in neighborhood thresholding |
| Application |
|---|
| • removal or labelling of outliers |
| • point cloud pre-processing |
Tool: Remove Low and Air Points (PC)
| Features |
|---|
| • removal of outliers from a point cloud by local neighborhood analysis (2D/3D) |
| • seperate elevation thresholds and class labels for low and air points |
| • detection of point groups/clusters (instead of individual points), optional |
| • iterative filtering (filter is applied several times), optional |
| Application |
|---|
| • removal of outliers from point clouds |
| • point cloud pre-processing |
Tool: Remove Outliers (PC)
| Features |
|---|
| • removal of outliers from a point cloud by local neighborhood analysis (k nearest neighbors) |
| • detection of negative outliers by elevation ranking and thresholding |
| • detection of positive outliers by elevation difference |
| Application |
|---|
| • removal of outliers from point clouds |
| • point cloud pre-processing |