# filters.estimaterank¶

filters.estimaterank uses singular value decomposition (SVD) to estimate the rank of a set of points. Point sets with rank 1 correspond to linear features, while sets with rank 2 correspond to planar features. Rank 3 corresponds to a full 3D feature. In practice this can be used alone, or possibly in conjunction with other filters to extract features (e.g., buildings, vegetation).

Two parameters are required to estimate rank (though the default values will be suitable in many cases). First, the knn parameter defines the number of points to consider when computing the SVD and estimated rank. Second, the thresh parameter is used to determine when a singular value shall be considered non-zero (when the absolute value of the singular value is greater than the threshold).

The rank estimation is performed on a pointwise basis, meaning for each point in the input point cloud, we find its knn neighbors, compute the SVD, and estimate rank. filters.estimaterank creates a new dimension called Rank that can be used downstream of this filter stage in the pipeline. The type of writer used will determine whether or not the Rank dimension itself can be saved to disk.

Default Embedded Stage

This stage is enabled by default

## Example¶

This sample pipeline estimates the rank of each point using filters.estimaterank and then filters out those points where the rank is three using filters.range.

{
"pipeline":[
"input.las",
{
"type":"filters.estimaterank",
"knn":8,
"thresh":0.01
},
{
"type":"filters.range",
"limits":"Rank![3:3]"
},
"output.laz"
]
}


## Options¶

knn
The number of k-nearest neighbors. [Default: 8]
thresh
The threshold used to identify nonzero singular values. [Default: 0.01]