calcFuzzyElsa_raster

Calculate the Local Fuzzy ELSA statistic for a numeric raster

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Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed.
The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Jeremy Gelb

geocmeans

Implementing Methods for Spatial Fuzzy Unsupervised
Classification

Philippe Apparicio

calcFuzzyElsa_raster function

<dl><dt>rasters</dt>
<dd>A List of SpatRaster or a List of matrices, or an array</dd>
<dt>window</dt>
<dd>A binary (0,1) matrix representing the neighbours spatial weights when working
with rasters. The matrix must have odd dimensions.</dd>
<dt>matdist</dt>
<dd>A matrix representing the dissimilarity between the clusters. The matrix must
be squared and the diagonal must be filled with zeros.</dd></dl>

Arguments

Local Fuzzy ELSA statistic for raster — calcFuzzyElsa_raster

<dl>

<dt>rasters</dt>
<dd>A List of SpatRaster or a List of matrices, or an array</dd>


<dt>window</dt>
<dd>A binary (0,1) matrix representing the neighbours spatial weights when working
with rasters. The matrix must have odd dimensions.</dd>


<dt>matdist</dt>
<dd>A matrix representing the dissimilarity between the clusters. The matrix must
be squared and the diagonal must be filled with zeros.</dd>

</dl>

calcFuzzyElsa_raster: Local Fuzzy ELSA statistic for raster

Description

Usage

Value

Arguments

Examples