Parent Topic: Supervised Algorithms
Parallelepiped Classifiers
The parallelepiped classifier uses the threshold of
each class signature to determine if a given
pixel falls within the class or not. The thresholds specifies
the dimensions (in standard deviation units) of each side of a
parallelepiped surrounding the mean of the class in feature
space. If the pixel falls inside the parallelepiped, it is
assigned to the class. However, if the pixel falls within more
than one class, it is put in the overlap class (code 255). If
the pixel does not fall inside any class, it is assigned to the
null class (code 0).
The parallelepiped classifier is typically used when speed is
required. Unfortunately, in many cases this results in poor accuracy and a
large number of pixels classified as ties (or overlap, class 255).
The parallelepiped classifier with maximum likelihood as a tie breaker is a
cross between the parallelepiped
classifier and the full maximum likelihood classifier. The
basic concept is to use parallelepiped classification unless
we have a tie (overlap), in which case the tie is resolved by
using full maximum likelihood classification.
This type of classification is an attempt to gain the speed of
the parallelepiped classifier while eliminating the large
number of pixels classed as ties (overlap).
Typically, the Ties approach is used as a preliminary step to the full
maximum likelihood classification.
Parent Topic: Supervised Algorithms
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