# filters.outlier¶

The Outlier filter provides two outlier filtering methods: radius and statistical. These two approaches are discussed in further detail below.

It is worth noting that both filtering methods simply apply a classification value of 7 to the noise points (per the LAS specification). To remove the noise points altogether, users can add a range filter to their pipeline, downstream from the outlier filter.

```
{
"type":"filters.range",
"limits":"Classification![7:7]"
}
```

## Statistical Method¶

The default method for identifying outlier points is the statistical outlier method. This method requires two passes through the input `PointView`

, first to compute a threshold value based on global statistics, and second to identify outliers using the computed threshold.

In the first pass, for each point \(p_i\) in the input `PointView`

, compute the mean distance \(\mu_i\) to each of the \(k\) nearest neighbors (where \(k\) is configurable and specified by `mean_k`

). Then,

A global mean \(\overline{\mu}\) of these mean distances is then computed along with the standard deviation \(\sigma\). From this, the threshold is computed as

where \(m\) is a user-defined multiplier specified by `multiplier`

.

We now interate over the pre-computed mean distances \(\mu_i\) and compare to computed threshold value. If \(\mu_i\) is greater than the threshold, it is marked as an outlier.

Before outlier removal, noise points can be found both above and below the scene.

After outlier removal, the noise points are removed.

See [Rusu2008] for more information.

### Example¶

In this example, points are marked as outliers if the average distance to each of the 12 nearest neighbors is below the computed threshold.

```
{
"pipeline":[
"input.las",
{
"type":"filters.outlier",
"method":"statistical",
"mean_k":12,
"multiplier":2.2
},
"output.las"
]
}
```

## Radius Method¶

For each point \(p_i\) in the input `PointView`

, this method counts the
number of neighboring points \(k_i\) within radius \(r\) (specified by
`radius`

). If \(k_i<k_{min}\), where \(k_{min}\) is the minimum number
of neighbors specified by `min_k`

, it is marked as an outlier.

### Example¶

The following example will mark points as outliers when there are fewer than four neighbors within a radius of 1.0.

```
{
"pipeline":[
"input.las",
{
"type":"filters.outlier",
"method":"radius",
"radius":1.0,
"min_k":4
},
"output.las"
]
}
```

## Options¶

- class
- The classification value to apply to outliers. [Default:
**7**] - method
- The outlier removal method. [Default:
**statistical**] - min_k
- Minimum number of neighbors in radius (radius method only). [Default:
**2**] - radius
- Radius (radius method only). [Default:
**1.0**] - mean_k
- Mean number of neighbors (statistical method only). [Default:
**8**] - multiplier
- Standard deviation threshold (statistical method only). [Default:
**2.0**]