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<H4>MAX_LIKELIHOOD</H4>
The full maximum likelihood classifier uses the Gaussian threshold
(THRS) stored in each class signature to determine if a given pixel
falls within the class or not. The threshold is the radius (in
standard deviation units) of a hyperellipse surrounding the mean of
the class in feature space. If the pixel falls inside the
hyperellipse, it is assigned to the class. The class bias (BIAS) is
used to resolve overlap between classes, and weights one class in
favour of another. If the pixel does not fall inside any class, it
is assigned to the null class (code 0).<p>
The maximum likelihood classifier is considered to give more
`accurate' results than parallelepiped classification however it is
much slower due to extra computations. We put the word `accurate' in
quotes because this assumes that classes in the input data have a
Gaussian distribution and that signatures were well selected; this
is not always a safe assumption.
<P>
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