# filters.estimaterank¶

The **rank estimation filter** 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. The filter 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.

## Example¶

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

```
[
"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]