First-order texture

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# Kurtosis
 
# Kurtosis
 
The standard deviation of gray levels in a region in the neighborhood of a pixel can be calculated by a GRASS GIS modul:
 
The standard deviation of gray levels in a region in the neighborhood of a pixel can be calculated by a GRASS GIS modul:
 +
Does currently not work in QGIS 3.4.
 
* In the search engine of the Processing Toolbox, type '''neighbors''' and select '''r.neighbors''' under Raster of GRASS GIS 7 commands.
 
* In the search engine of the Processing Toolbox, type '''neighbors''' and select '''r.neighbors''' under Raster of GRASS GIS 7 commands.
 
* Under the Parameters tab, select a single band file as input layer.
 
* Under the Parameters tab, select a single band file as input layer.

Revision as of 19:19, 18 November 2018

Local statistical moments (Mean, Variance, Skewness, Kurtosis) calculated on every pixel in the selected channel of the input image, over a specified neighborhood are called first-order textures.

  • In the search engine of the Processing Toolbox, type Local Statistic and select LocalStatisticExtraction under Feature Extraction of OTB.
  • Under the Parameters tab, select a single band or a multiband file as input layer.
  • In case of a multiband file select the band number.
  • Select 3 as Neighborhood radius in pixels.

Qgis texture Localstatistics.png

The output image (Fig. B) is a multiband with 4 statistical moments per band in the order:

  1. Mean
  2. Variance
  3. Skewness
  4. Kurtosis

The standard deviation of gray levels in a region in the neighborhood of a pixel can be calculated by a GRASS GIS modul: Does currently not work in QGIS 3.4.

  • In the search engine of the Processing Toolbox, type neighbors and select r.neighbors under Raster of GRASS GIS 7 commands.
  • Under the Parameters tab, select a single band file as input layer.
  • Select stddev from the drop-down list Neighborhood operation.
  • Select an odd integer number as Neighborhood size in pixels.
  • Tick Use circular neighborhood.

Qgis texture stdev.png

Figure A: Input image: Sentinel-2 synthesis image based on 4 10m bands, (University Göttingen Campus North)
Figure B: Output image: First-order textures of Sentinel-2 synthesis image RGB=Skewness, Variance, Mean (University Göttingen Campus North)
Figure C: Output image: Standard deviation texture of Sentinel-2 synthesis image (University Göttingen Campus North)
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