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Landsat image classification using a...

Zheng, Jian.

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Landsat image classification using a neuro-fuzzy system.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Landsat image classification using a neuro-fuzzy system./
Author:	Zheng, Jian.
Description:	37 p.
Notes:	Source: Masters Abstracts International, Volume: 41-02, page: 0412.
Contained By:	Masters Abstracts International41-02.
Subject:	Geography. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1410455
ISBN:	0493774270

Landsat image classification using a neuro-fuzzy system.
Zheng, Jian.

Landsat image classification using a neuro-fuzzy system. - 37 p.

Source: Masters Abstracts International, Volume: 41-02, page: 0412.

Thesis (M.A.)--San Jose State University, 2002.

This study investigates an alternative classification algorithm, NEFCLASS, and its ability to classify remote sensing images. NEFCLASS is a Neuro-fuzzy System that is capable of generating a set of linguistic rules. These rules allow the user to check and interpret the classification results. This study also shows that the neural net rules stabilized after only a few training iterations. The land-use/land-cover classification result produced by NEFCLASS is compared to the result produced by a conventional classification algorithm, Maximum Likelihood Classifier (MLC). NEFCLASS produced better classification accuracy than MLC.

ISBN: 0493774270Subjects--Topical Terms:

524010
Geography.

Landsat image classification using a neuro-fuzzy system.
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100 1 $a Zheng, Jian. $3 1953606
245 1 0 $a Landsat image classification using a neuro-fuzzy system.
300 $a 37 p.
500 $a Source: Masters Abstracts International, Volume: 41-02, page: 0412.
500 $a Adviser: Richard Taketa.
502 $a Thesis (M.A.)--San Jose State University, 2002.
520 $a This study investigates an alternative classification algorithm, NEFCLASS, and its ability to classify remote sensing images. NEFCLASS is a Neuro-fuzzy System that is capable of generating a set of linguistic rules. These rules allow the user to check and interpret the classification results. This study also shows that the neural net rules stabilized after only a few training iterations. The land-use/land-cover classification result produced by NEFCLASS is compared to the result produced by a conventional classification algorithm, Maximum Likelihood Classifier (MLC). NEFCLASS produced better classification accuracy than MLC.
590 $a School code: 6265.
650 4 $a Geography. $3 524010
650 4 $a Geodesy. $3 550741
650 4 $a Computer Science. $3 626642
650 4 $a Remote Sensing. $3 1018559
650 4 $a Artificial Intelligence. $3 769149
690 $a 0366
690 $a 0370
690 $a 0984
690 $a 0799
690 $a 0800
710 2 0 $a San Jose State University. $3 1020800
773 0 $t Masters Abstracts International $g 41-02.
790 1 0 $a Taketa, Richard, $e advisor
790 $a 6265
791 $a M.A.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1410455