Multistage classification of multispectral earth observational data: the design approach.

M. E. Bauer; M. J. Muasher; D. A. Landgrebe

Multistage classification of multispectral earth observational data: the design approach.

M. E. Bauer
, M. J. Muasher
, D. A. Landgrebe

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Abstract

An algorithm is proposed which predicts the optimal features of every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. -from NASA Abstract E82-10213

Original language	English (US)
Title of host publication	Unknown Host Publication Title
Publisher	Purdue University, Lafayette, IN, Laboratory for Applications of Remote Sensing, LARS-101481
State	Published - 1981

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title = "Multistage classification of multispectral earth observational data: the design approach.",

abstract = "An algorithm is proposed which predicts the optimal features of every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. -from NASA Abstract E82-10213",

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T1 - Multistage classification of multispectral earth observational data

T2 - the design approach.

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AU - Muasher, M. J.

AU - Landgrebe, D. A.

PY - 1981

Y1 - 1981

N2 - An algorithm is proposed which predicts the optimal features of every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. -from NASA Abstract E82-10213

AB - An algorithm is proposed which predicts the optimal features of every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. -from NASA Abstract E82-10213

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M3 - Chapter

BT - Unknown Host Publication Title

PB - Purdue University, Lafayette, IN, Laboratory for Applications of Remote Sensing, LARS-101481

ER -

Multistage classification of multispectral earth observational data: the design approach.

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