31 January 1995 Classification of forestry species using singular value decomposition
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sean Danaher, Graham M. Herries, M. MacSiurtain, and Eon O'Mongain "Classification of forestry species using singular value decomposition", Proc. SPIE 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources, (31 January 1995); doi: 10.1117/12.200768; https://doi.org/10.1117/12.200768
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