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Evaluating neural networks and evidence pooling for land cover mapping

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Evaluating neural networks and evidence pooling for land cover mapping. / Aitkenhead, M. J.; Flaherty, S.; Cutler, M. E. J.

In: Photogrammetric Engineering and Remote Sensing, Vol. 74, No. 8, 08.2008, p. 1019-1032.

Research output: Contribution to journalArticle

Harvard

Aitkenhead, MJ, Flaherty, S & Cutler, MEJ 2008, 'Evaluating neural networks and evidence pooling for land cover mapping' Photogrammetric Engineering and Remote Sensing, vol 74, no. 8, pp. 1019-1032.

APA

Aitkenhead, M. J., Flaherty, S., & Cutler, M. E. J. (2008). Evaluating neural networks and evidence pooling for land cover mapping. Photogrammetric Engineering and Remote Sensing, 74(8), 1019-1032

Vancouver

Aitkenhead MJ, Flaherty S, Cutler MEJ. Evaluating neural networks and evidence pooling for land cover mapping. Photogrammetric Engineering and Remote Sensing. 2008 Aug;74(8):1019-1032.

Author

Aitkenhead, M. J.; Flaherty, S.; Cutler, M. E. J. / Evaluating neural networks and evidence pooling for land cover mapping.

In: Photogrammetric Engineering and Remote Sensing, Vol. 74, No. 8, 08.2008, p. 1019-1032.

Research output: Contribution to journalArticle

Bibtex - Download

@article{9646c12fd5404ca787b07e13f0e9f870,
title = "Evaluating neural networks and evidence pooling for land cover mapping",
author = "Aitkenhead, {M. J.} and S. Flaherty and Cutler, {M. E. J.}",
year = "2008",
volume = "74",
number = "8",
pages = "1019--1032",
journal = "Photogrammetric Engineering and Remote Sensing",
issn = "0099-1112",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Evaluating neural networks and evidence pooling for land cover mapping

A1 - Aitkenhead,M. J.

A1 - Flaherty,S.

A1 - Cutler,M. E. J.

AU - Aitkenhead,M. J.

AU - Flaherty,S.

AU - Cutler,M. E. J.

PY - 2008/8

Y1 - 2008/8

N2 - The diversity of data sources, analysis methodologies, and classification systems has led to a number of new techniques for monitoring land-cover change. However, this wide choice means that it is difficult to know which solution to choose. A system capable of integrating the results of different analyses and applying them to land-cover mapping would therefore be extremely useful. This study investigates the use of evidence pooling and neural networks in land-cover mapping. Neural networks were used to classify land-cover using evidence from spectral (Landsat-7 ETM1), textural, and topographic information. Mapping was performed using combinations of evidence source and evidence pooling techniques. The best performance was achieved using all available information with a method that summed evidence directly instead of categorizing it. While the methodology failed to reach the level of accuracy recommended elsewhere, a comparison of the number of classes used with other methods showed that the system performed better than these approaches. © 2008 American Society for Photogrammetry and Remote Sensing.

AB - The diversity of data sources, analysis methodologies, and classification systems has led to a number of new techniques for monitoring land-cover change. However, this wide choice means that it is difficult to know which solution to choose. A system capable of integrating the results of different analyses and applying them to land-cover mapping would therefore be extremely useful. This study investigates the use of evidence pooling and neural networks in land-cover mapping. Neural networks were used to classify land-cover using evidence from spectral (Landsat-7 ETM1), textural, and topographic information. Mapping was performed using combinations of evidence source and evidence pooling techniques. The best performance was achieved using all available information with a method that summed evidence directly instead of categorizing it. While the methodology failed to reach the level of accuracy recommended elsewhere, a comparison of the number of classes used with other methods showed that the system performed better than these approaches. © 2008 American Society for Photogrammetry and Remote Sensing.

KW - Artificial intelligence

KW - Computer networks

KW - Conformal mapping

KW - Accuracy assessment

KW - Neural networks

KW - Classification

KW - Land cover

KW - Landsat

KW - Topographic mapping

UR - http://www.asprs.org/PE-RS-Journals-2008/PE-RS-August-2008.html

M1 - Article

JO - Photogrammetric Engineering and Remote Sensing

JF - Photogrammetric Engineering and Remote Sensing

SN - 0099-1112

IS - 8

VL - 74

SP - 1019

EP - 1032

ER -

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