Overview

Textory can be used to calculate correlation statistics like variogram as an image texture. Basically the statistic is calculated for a moving window over the whole array. Each pixel in the resulting array is the statistical measure of the small window around the pixel.

These type of image textures are especially interesting for machine learning (ML) approaches on spatial datasets like satellite data, since they are able to include spatial information within single bands or even between two different datasets into the ML model.

Features

Textory is able to calculate:

• Variogram: \(\gamma(h) = \frac{1}{2n(h)} \sum_{i=1}^{n(h)} (v(x_{i}) - v(x_{i}+h))^{2}\)
• Madogram: \(\gamma(h) = \frac{1}{2n(h)} \sum_{i=1}^{n(h)} |v(x_{i}) - v(x_{i}+h)|\)
• Rodogram: \(\gamma(h) = \frac{1}{2n(h)} \sum_{i=1}^{n(h)} \sqrt{|v(x_{i}) - v(x_{i}+h)|}\)
• Cross Variogram: \(\gamma(h) = \frac{1}{2n(h)} \sum_{i=1}^{n(h)} (v(x_{i}) - v(x_{i}+h))*(w(x_{i}) - w(x_{i}+h))\)
• Pseudo Cross Variogram: \(\gamma(h) = \frac{1}{2n(h)} \sum_{i=1}^{n(h)} (v(x_{i}) - w(x_{i}+h))^{2}\)
• basic statistics (e.g. min, max, median, etc. (only for square windows))
• TPI (Topographic Position Index) for different window sizes and geometries.

for different lag distances and window sizes (round and square windows) for numpy and dask.array.Array as well as xarray.DataArray. Furthermore convenient functions to easily caluculate these statistics for xarray.Dataset and satpy.scene.Scene are available.