MLC.model-class

The maximum likelihood classifier (MLC) is one of the most common classifiers used for
remote sensing imagery.
This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train
and predict over tabular data (data.frame).
The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.

Caio Hamamura

tabularMLC

Tabular Maximum Likelihood Classifier

MLC.model-class function

<dl>
<dt><code>k</code></dt>
<dd>the constant fraction to be used in model \(\frac{1}{(2 \pi)^{\frac{L}{2}} \sqrt{\left | \Sigma_i \right |}}\)</dd></dl>
<dt><code>mu</code></dt>
<dd>mean (\(\mu_i\)) list for each variable and class</dd>
<dt><code>inverseCovarianceMatrices</code></dt>
<dd>inverted covariance matrix (\(\Sigma_i\)) for each class</dd>
<dt><code>groups</code></dt>
<dd>the classification levels</dd>
<dt><code>vars</code></dt>
<dd>the variables used for trainning the model</dd>

Slots

Maximum likelihood model class — MLC.model-class

<dl>
<dt><code>k</code></dt>
<dd>the constant fraction to be used in model \(\frac{1}{(2 \pi)^{\frac{L}{2}} \sqrt{\left | \Sigma_i \right |}}\)</dd>


<dt><code>mu</code></dt>
<dd>mean (\(\mu_i\)) list for each variable and class</dd>


<dt><code>inverseCovarianceMatrices</code></dt>
<dd>inverted covariance matrix (\(\Sigma_i\)) for each class</dd>


<dt><code>groups</code></dt>
<dd>the classification levels</dd>


<dt><code>vars</code></dt>
<dd>the variables used for trainning the model</dd>


</dl>

MLC.model-class: Maximum likelihood model class

Description

Arguments

Slots

See Also