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Evaluating neural networks and evidence pooling for land cover mapping

Evaluating neural networks and evidence pooling for land cover mapping

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Original languageEnglish
Pages1019-1032
Number of pages14
JournalPhotogrammetric Engineering and Remote Sensing
Journal publication dateAug 2008
Journal number8
Volume74
StatePublished

Abstract

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.

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