An artificial neural network (ANN) based software package for classification of remotely sensed data

This paper presents a package of C programs for classification of remotely sensed data using an artificial neural network (ANN) approach. The ANN used is a multilayer perceptron trained through the generalized delta learning rule. The software package is generalized in nature and can handle any numb...

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Bibliographic Details
Published inComputers & geosciences Vol. 22; no. 1; pp. 81 - 87
Main Authors Mohanty, K.K., Majumdar, T.J.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.02.1996
Elsevier Science
Subjects
Artificial neural network
Classification
Earth sciences
Earth, ocean, space
Exact sciences and technology
General geology
Remotely sensed data
C
Artificial neural network
Remotely sensed data
Classification
remote sensing
data
classification
artificial intelligence
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Summary:This paper presents a package of C programs for classification of remotely sensed data using an artificial neural network (ANN) approach. The ANN used is a multilayer perceptron trained through the generalized delta learning rule. The software package is generalized in nature and can handle any number of input units (spectral bands), output units (feature classes) and hidden layers. Different numbers of hidden neurons also can be considered in various hidden layers. An application of the software package for classification of IRS-1A LISS-I images also has been demonstrated.
ISSN:0098-3004
1873-7803
DOI:10.1016/0098-3004(95)00059-3