Rough Sets for Satellite Image Classification

Language: English

Published by LAP LAMBERT Academic Publishing Jan 2017, 2017

3330032820 / 9783330032828

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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 Landstraße 99, 66123 Saarbrücken 108 pp. Englisch. …

Seller Inventory # 9783330032828

Title
Rough Sets for Satellite Image Classification
Author
Sonika Jindal
Publisher
LAP LAMBERT Academic Publishing Jan 2017
Publication year
2017
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3330032820
ISBN 13
9783330032828
Item weight
179 grams
Dimensions
220x150x8 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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