Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.

A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2...

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Autores principales:	Luis Garcia-Torres, Juan J Caballero-Novella, David Gómez-Candón, Ana Isabel De-Castro
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3b31c2155c094172a5e9978d445b5f3a
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spelling	oai:doaj.org-article:3b31c2155c094172a5e9978d445b5f3a2021-11-18T08:29:17ZSemi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.1932-620310.1371/journal.pone.0091275https://doaj.org/article/3b31c2155c094172a5e9978d445b5f3a2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24604031/?tool=EBIhttps://doaj.org/toc/1932-6203A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified.Luis Garcia-TorresJuan J Caballero-NovellaDavid Gómez-CandónAna Isabel De-CastroPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e91275 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luis Garcia-Torres Juan J Caballero-Novella David Gómez-Candón Ana Isabel De-Castro Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
description	A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified.
format	article
author	Luis Garcia-Torres Juan J Caballero-Novella David Gómez-Candón Ana Isabel De-Castro
author_facet	Luis Garcia-Torres Juan J Caballero-Novella David Gómez-Candón Ana Isabel De-Castro
author_sort	Luis Garcia-Torres
title	Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
title_short	Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
title_full	Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
title_fullStr	Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
title_full_unstemmed	Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
title_sort	semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/3b31c2155c094172a5e9978d445b5f3a
work_keys_str_mv	AT luisgarciatorres semiautomaticnormalizationofmultitemporalremoteimagesbasedonvegetativepseudoinvariantfeatures AT juanjcaballeronovella semiautomaticnormalizationofmultitemporalremoteimagesbasedonvegetativepseudoinvariantfeatures AT davidgomezcandon semiautomaticnormalizationofmultitemporalremoteimagesbasedonvegetativepseudoinvariantfeatures AT anaisabeldecastro semiautomaticnormalizationofmultitemporalremoteimagesbasedonvegetativepseudoinvariantfeatures
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Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.

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