This tool calculates the percentile of the center cell in a moving filter window applied to an input image (`input). This indicates the value below which a given percentage of the neighbouring values in within the filter fall. For example, the 35th percentile is the value below which 35% of the neighbouring values in the filter window may be found. As such, the percentile of a pixel value is indicative of the relative location of the site within the statistical distribution of values contained within a filter window. When applied to input digital elevation models, percentile is a measure of local topographic position, or elevation residual.

Neighbourhood size, or filter size, is specified in the x and y dimensions using the `filterx` and `filtery` flags. These dimensions should be odd, positive integer values, e.g. 3, 5, 7, 9... If the kernel filter size is the same in the x and y dimensions, the silent `filter` flag may be used instead (command-line interface only).

This tool takes advantage of the redundancy between overlapping, neighbouring filters to enhance computationally efficiency, using a method similar to Huang et al. (1979). This efficient method of calculating percentiles requires rounding of floating-point inputs, and therefore the user must specify the number of significant digits (`sig_digits`) to be used during the processing. Like most of WhiteboxTools' filters, this tool is also parallelized for further efficiency.

## Reference

Huang, T., Yang, G.J.T.G.Y. and Tang, G., 1979. A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(1), pp.13-18.

## See Also

median_filter

## Function Signature

`def percentile_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...`

## Project Links

WbW Homepage User Manual Support WbW