Rough Sets for Satellite Image Classification: Landuse/Landcover Analysis of Alwar (Rajasthan) - Couverture souple

Jindal, Sonika

9783330032828: Rough Sets for Satellite Image Classification: Landuse/Landcover Analysis of Alwar (Rajasthan)

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ISBN 10 : 3330032820 ISBN 13 : 9783330032828

Editeur : LAP LAMBERT Academic Publishing, 2017

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It has been observed that the ground truth data, forming a prime decision system, an essential ingredient for a supervised learning, may itself contain redundant / inconsistent / conflicting information. Moreover, there may be superfluous attributes that warrants a fast mechanism to identify & discard them and at the same time keep the information content compatible to the original data set. The Rough Set Theory has emerged as an effective measure to resolve imprecise knowledge, analysis of conflicts, evaluation of data dependencies and generating rules. Landuse/Landcover classification is specifically chosen for this study as it is well recognized important task in landscape ecology. The objective is how the decision system required for any supervised classification, is made consistent and free from superfluous attributes. Land cover classification of the LISS-III image pertaining to Alwar (India) area by the Rough Set, Maximum Likelihood Classifier and Minimum Distance Classifier is performed. The findings show that, in the era of internet GIS where time and accuracy is the prime requirement in classification of images, Rough set theory offers better and accurate results.

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2017
Langue: anglais
ISBN 10: 3330032820
ISBN 13: 9783330032828
Reliure: Broch�
Nombre de pages: 108
Coordonn�es du fabricant: non disponible
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Rough Sets for Satellite Image Classification

Sonika Jindal

Edit� par LAP LAMBERT Academic Publishing Jan 2017, 2017

ISBN 10 : 3330032820 ISBN 13 : 9783330032828

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -It has been observed that the ground truth data, forming a prime decision system, an essential ingredient for a supervised learning, may itself contain redundant / inconsistent / conflicting information. Moreover, there may be superfluous attributes that warrants a fast mechanism to identify & discard them and at the same time keep the information content compatible to the original data set. The Rough Set Theory has emerged as an effective measure to resolve imprecise knowledge, analysis of conflicts, evaluation of data dependencies and generating rules. Landuse/Landcover classification is specifically chosen for this study as it is well recognized important task in landscape ecology. The objective is how the decision system required for any supervised classification, is made consistent and free from superfluous attributes. Land cover classification of the LISS-III image pertaining to Alwar (India) area by the Rough Set, Maximum Likelihood Classifier and Minimum Distance Classifier is performed. The findings show that, in the era of internet GIS where time and accuracy is the prime requirement in classification of images, Rough set theory offers better and accurate results. 108 pp. Englisch. N� de r�f. du vendeur 9783330032828

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Rough Sets for Satellite Image Classification

Sonika Jindal

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ISBN 10 : 3330032820 ISBN 13 : 9783330032828

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jindal SonikaThe author is presently working as Assistant Professor in CSE at Shaheed Bhagat Singh State Technical Campus, Ferozepur, India. She has around 18 years experience in teaching. She has supervised several UG and PG Researc. N� de r�f. du vendeur 158122946

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Rough Sets for Satellite Image Classification

Sonika Jindal

Edit� par LAP LAMBERT Academic Publishing Jan 2017, 2017

ISBN 10 : 3330032820 ISBN 13 : 9783330032828

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -It has been observed that the ground truth data, forming a prime decision system, an essential ingredient for a supervised learning, may itself contain redundant / inconsistent / conflicting information. Moreover, there may be superfluous attributes that warrants a fast mechanism to identify & discard them and at the same time keep the information content compatible to the original data set. The Rough Set Theory has emerged as an effective measure to resolve imprecise knowledge, analysis of conflicts, evaluation of data dependencies and generating rules. Landuse/Landcover classification is specifically chosen for this study as it is well recognized important task in landscape ecology. The objective is how the decision system required for any supervised classification, is made consistent and free from superfluous attributes. Land cover classification of the LISS-III image pertaining to Alwar (India) area by the Rough Set, Maximum Likelihood Classifier and Minimum Distance Classifier is performed. The findings show that, in the era of internet GIS where time and accuracy is the prime requirement in classification of images, Rough set theory offers better and accurate results.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 108 pp. Englisch. N� de r�f. du vendeur 9783330032828

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Rough Sets for Satellite Image Classification : Landuse/Landcover Analysis of Alwar (Rajasthan)

Sonika Jindal

Edit� par LAP LAMBERT Academic Publishing, 2017

ISBN 10 : 3330032820 ISBN 13 : 9783330032828

Neuf Taschenbuch

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - It has been observed that the ground truth data, forming a prime decision system, an essential ingredient for a supervised learning, may itself contain redundant / inconsistent / conflicting information. Moreover, there may be superfluous attributes that warrants a fast mechanism to identify & discard them and at the same time keep the information content compatible to the original data set. The Rough Set Theory has emerged as an effective measure to resolve imprecise knowledge, analysis of conflicts, evaluation of data dependencies and generating rules. Landuse/Landcover classification is specifically chosen for this study as it is well recognized important task in landscape ecology. The objective is how the decision system required for any supervised classification, is made consistent and free from superfluous attributes. Land cover classification of the LISS-III image pertaining to Alwar (India) area by the Rough Set, Maximum Likelihood Classifier and Minimum Distance Classifier is performed. The findings show that, in the era of internet GIS where time and accuracy is the prime requirement in classification of images, Rough set theory offers better and accurate results. N� de r�f. du vendeur 9783330032828

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