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Article: Conifer species recognition: Effects of data transformation

TitleConifer species recognition: Effects of data transformation
Authors
Issue Date2001
Citation
International Journal of Remote Sensing, 2001, v. 22, n. 17, p. 3471-3481 How to Cite?
AbstractIn situ hyperspectral data obtained with a high spectral resolution radiometer were analysed for identification of six conifer species. Hyperspectral data were measured in the summer and late fall seasons at 15-20 cm above portions of tree canopies from both the sunlit and shaded sides. An artificial neural network algorithm was applied for identification purposes. Six types of transformation were applied to the hyperspectral reflectance data (R). pre-processed with a simple smoothing, followed by band aggregation. These include log(R), first derivative of R, first derivative of log(R), normalized R, first derivative of normalized R, and log(normalized R). First derivative of log(R) and first derivative of normalized R resulted in best species recognition accuracies with greater than 90% average accuracies, more than 20% greater than the average accuracy obtained from the pre-processed hyperspectral data. The effect of hyperspectral data taken from the shade sides of tree canopies can be minimized by applying normalization or by taking the derivatives after applying a logarithm to the pre-processed data. We found that a big difference in solar angle did not cause a noticeable difference in accuracies of species recognition. © 2001 Taylor & Francis.
Persistent Identifierhttp://hdl.handle.net/10722/296924
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 0.776
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGong, P.-
dc.contributor.authorPu, R.-
dc.contributor.authorYu, B.-
dc.date.accessioned2021-02-25T15:16:59Z-
dc.date.available2021-02-25T15:16:59Z-
dc.date.issued2001-
dc.identifier.citationInternational Journal of Remote Sensing, 2001, v. 22, n. 17, p. 3471-3481-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296924-
dc.description.abstractIn situ hyperspectral data obtained with a high spectral resolution radiometer were analysed for identification of six conifer species. Hyperspectral data were measured in the summer and late fall seasons at 15-20 cm above portions of tree canopies from both the sunlit and shaded sides. An artificial neural network algorithm was applied for identification purposes. Six types of transformation were applied to the hyperspectral reflectance data (R). pre-processed with a simple smoothing, followed by band aggregation. These include log(R), first derivative of R, first derivative of log(R), normalized R, first derivative of normalized R, and log(normalized R). First derivative of log(R) and first derivative of normalized R resulted in best species recognition accuracies with greater than 90% average accuracies, more than 20% greater than the average accuracy obtained from the pre-processed hyperspectral data. The effect of hyperspectral data taken from the shade sides of tree canopies can be minimized by applying normalization or by taking the derivatives after applying a logarithm to the pre-processed data. We found that a big difference in solar angle did not cause a noticeable difference in accuracies of species recognition. © 2001 Taylor & Francis.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleConifer species recognition: Effects of data transformation-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01431160110034654-
dc.identifier.scopuseid_2-s2.0-0035923252-
dc.identifier.volume22-
dc.identifier.issue17-
dc.identifier.spage3471-
dc.identifier.epage3481-
dc.identifier.isiWOS:000172029800016-
dc.identifier.issnl0143-1161-

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