Classification of forestry species using singular value decomposition

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Title: Classification of forestry species using singular value decomposition
Authors: Danaher, Sean
Herries, Graham M.
Mac Siúrtáin, Máirtín Pádraig
O'Mongain, E.
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Date: Sep-1994
Abstract: A method is defined and tested for the classification of forest species from multi-spectral data, based on singular value decomposition (SVD) and key vector analysis. The SVD technique, which bears a close resemblance to multivariate statistic techniques has previously been successfully applied to the problem of signal extraction from marine data. In this study the SVD technique is used as a classifier for forest regions, using SPOT and landsat thematic mapper data. The specific region chosen is in the County Wicklow area of Ireland. This area has a large number of species, within a very small region and hence is not amenable to existing techniques. Preliminary results indicate that SVD is a fast and efficient classifier with the ability to differentiate between species such as Scots pine, Japanese larch and Sitka spruce. Classification accuracy's using this technique yielded excellent results of > 99% for forest, against four background classes. The accuracy's of the individual species classification are slightly lower, but they are still high at 97 - 100% for the SPOT wavebands. When the Landsat TM bands 3, 4, and 5 were used on their own, accuracies of 95 - 100% were achieved.
Type of material: Journal Article
Publisher: The International Society for Optical Engineering
Copyright (published version): 1995 SPIE--The International Society for Optical Engineering
Keywords: DemodulationForestry
DOI: 10.1117/12.200768
Language: en
Status of Item: Not peer reviewed
Is part of: Eric Mougin; K. Jon Ranson; James A. Smith (eds.). Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources (SPIE proceedings, vol. 2314)
Conference Details: Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources, Rome, Italy, September 26, 1994
Appears in Collections:Agriculture and Food Science Research Collection

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