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An improved methodology for land-cov...

Arellano-Neri, Olimpia.

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An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier./
作者:	Arellano-Neri, Olimpia.
面頁冊數:	131 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 3922.
Contained By:	Dissertation Abstracts International65-08B.
標題:	Physical Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3146490
ISBN:	0496043005

An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier.
Arellano-Neri, Olimpia.

An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier. - 131 p.

Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 3922.

Thesis (Ph.D.)--University of Cincinnati, 2004.

Mapping is essential for the analysis of the land and land-cover dynamics, which influence many environmental processes and properties. When creating land-cover maps it is important to minimize error, since error will propagate into later analys

ISBN: 0496043005Subjects--Topical Terms:

893400
Physical Geography.

An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier.
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245 1 3 $a An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier.
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500 $a Source: Dissertation Abstracts International, Volume: 65-08, Section: B, page: 3922.
500 $a Chair: Robert C. Frohn.
502 $a Thesis (Ph.D.)--University of Cincinnati, 2004.
520 $a Mapping is essential for the analysis of the land and land-cover dynamics, which influence many environmental processes and properties. When creating land-cover maps it is important to minimize error, since error will propagate into later analys
520 $a For decades, traditional statistical methods have been applied in land-cover classification with varying degrees of accuracy. One of the most significant developments in the field of land-cover classification using remotely sensed data has been
520 $a In this research, Artificial Neural Networks were applied to remotely sensed data of the southwestern Ohio region for land-cover classification. Three variants on traditional ANN-based classifiers were explored here: (1) the use of a customized
520 $a The objective of this research was to prove that a classification based on Artificial Neural Networks (ANN) and decision tree (DT) would outperform by far the National Land Cover Data (NLCD). The NLCD is a land-cover classification produced by a
590 $a School code: 0045.
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650 4 $a Geological Survey. $3 1256399
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