Please use this identifier to cite or link to this item: http://117.252.14.250:8080/jspui/handle/123456789/6704
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dc.contributor.authorMohanty, K. K.-
dc.date.accessioned2022-03-08T20:27:02Z-
dc.date.available2022-03-08T20:27:02Z-
dc.date.issued1998-
dc.identifier.urihttp://117.252.14.250:8080/jspui/handle/123456789/6704-
dc.description.abstractThis paper presents a preliminary study on usage of Artificial Neural Network (ANN) for land cover classification using multispectral remotely sensed data. The ANN model used is a simple multi-layer perceptron trained through generalized delta learning rule. Performance of the ANN classifier has been assessed with reference to the conventional maximum likelihood (MXL) classifier. In the current study, it is found that the ANN classifier has a better classification accuracy than the MXL classifier.en_US
dc.language.isoenen_US
dc.publisherNational Institute of Hydrologyen_US
dc.subjectRemote Sensingen_US
dc.subjectGISen_US
dc.subjectArtificial Neural Networken_US
dc.title6-An Artificial Neural Network Approach for Land Cover Classification.en_US
dc.typeTechnical Reporten_US
Appears in Collections:18-Jal Vigyan Sameeksha Vol.-13(1-2)-1998

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