Spectral signature generalization and expansion can improve the accuracy of satellite image classification.

Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) sig...

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Autores principales:	Alice G Laborte, Aileen A Maunahan, Robert J Hijmans
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c55f3a2b13404b0589092ac38520f607
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spelling	oai:doaj.org-article:c55f3a2b13404b0589092ac38520f6072021-12-02T20:21:52ZSpectral signature generalization and expansion can improve the accuracy of satellite image classification.1932-620310.1371/journal.pone.0010516https://doaj.org/article/c55f3a2b13404b0589092ac38520f6072010-05-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20463895/?tool=EBIhttps://doaj.org/toc/1932-6203Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification.Alice G LaborteAileen A MaunahanRobert J HijmansPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 5, p e10516 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alice G Laborte Aileen A Maunahan Robert J Hijmans Spectral signature generalization and expansion can improve the accuracy of satellite image classification.
description	Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification.
format	article
author	Alice G Laborte Aileen A Maunahan Robert J Hijmans
author_facet	Alice G Laborte Aileen A Maunahan Robert J Hijmans
author_sort	Alice G Laborte
title	Spectral signature generalization and expansion can improve the accuracy of satellite image classification.
title_short	Spectral signature generalization and expansion can improve the accuracy of satellite image classification.
title_full	Spectral signature generalization and expansion can improve the accuracy of satellite image classification.
title_fullStr	Spectral signature generalization and expansion can improve the accuracy of satellite image classification.
title_full_unstemmed	Spectral signature generalization and expansion can improve the accuracy of satellite image classification.
title_sort	spectral signature generalization and expansion can improve the accuracy of satellite image classification.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/c55f3a2b13404b0589092ac38520f607
work_keys_str_mv	AT aliceglaborte spectralsignaturegeneralizationandexpansioncanimprovetheaccuracyofsatelliteimageclassification AT aileenamaunahan spectralsignaturegeneralizationandexpansioncanimprovetheaccuracyofsatelliteimageclassification AT robertjhijmans spectralsignaturegeneralizationandexpansioncanimprovetheaccuracyofsatelliteimageclassification
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Spectral signature generalization and expansion can improve the accuracy of satellite image classification.

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