A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery

Hyperspectral remote sensors acquire data coming from hundreds of narrow bands through the electromagnetic spectrum; this allows the terrestrial and maritime surfaces to be characterized for Earth observation. Hyperspectral image processing requires algorithms that combine spatial and spectral infor...

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Autores:
Torres-Madronero, Maria Constanza
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/60414
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/60414
http://bdigital.unal.edu.co/58746/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
difusión no lineal
árbol de partición binaria
clasificación
imagen hiperespectral
representación multiescala
percepción remota
nonlinear diffusion
binary partition tree
classification
hyperspectral imagery
multiscale representation
remote sensing
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_d618c68a2a49eb2a21d32450b274f64d
oai_identifier_str oai:repositorio.unal.edu.co:unal/60414
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
title A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
spellingShingle A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
62 Ingeniería y operaciones afines / Engineering
difusión no lineal
árbol de partición binaria
clasificación
imagen hiperespectral
representación multiescala
percepción remota
nonlinear diffusion
binary partition tree
classification
hyperspectral imagery
multiscale representation
remote sensing
title_short A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
title_full A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
title_fullStr A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
title_full_unstemmed A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
title_sort A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery
dc.creator.fl_str_mv Torres-Madronero, Maria Constanza
dc.contributor.author.spa.fl_str_mv Torres-Madronero, Maria Constanza
dc.subject.ddc.spa.fl_str_mv 62 Ingeniería y operaciones afines / Engineering
topic 62 Ingeniería y operaciones afines / Engineering
difusión no lineal
árbol de partición binaria
clasificación
imagen hiperespectral
representación multiescala
percepción remota
nonlinear diffusion
binary partition tree
classification
hyperspectral imagery
multiscale representation
remote sensing
dc.subject.proposal.spa.fl_str_mv difusión no lineal
árbol de partición binaria
clasificación
imagen hiperespectral
representación multiescala
percepción remota
nonlinear diffusion
binary partition tree
classification
hyperspectral imagery
multiscale representation
remote sensing
description Hyperspectral remote sensors acquire data coming from hundreds of narrow bands through the electromagnetic spectrum; this allows the terrestrial and maritime surfaces to be characterized for Earth observation. Hyperspectral image processing requires algorithms that combine spatial and spectral information. One way to take full advantage of spatial-spectral data within hyperspectral imagery is to use multiscale representations. A multiscale representation generates a family of images were fine details are systematically removed. This paper compares two multiscale representation approaches in order to improve the classification of hyperspectral imagery. The first approach is based on nonlinear diffusion, which obtains a multiscale representation by successive filtering. The second is based on binary partition tree, an approach inspired in region growing. The comparison is performed using a real hyperspectral image and a supper vector machine classifier. Both representation approaches allowed the classification of hyperspectral imagery to be improved. However, nonlinear diffusion results surpassed those obtained using binary partition tree.
publishDate 2017
dc.date.issued.spa.fl_str_mv 2017-01-01
dc.date.accessioned.spa.fl_str_mv 2019-07-02T18:15:47Z
dc.date.available.spa.fl_str_mv 2019-07-02T18:15:47Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
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dc.identifier.issn.spa.fl_str_mv ISSN: 2346-2183
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/60414
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/58746/
identifier_str_mv ISSN: 2346-2183
url https://repositorio.unal.edu.co/handle/unal/60414
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dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/dyna/article/view/50678
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Dyna
Dyna
dc.relation.references.spa.fl_str_mv Torres-Madronero, Maria Constanza (2017) A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery. DYNA, 84 (200). pp. 129-134. ISSN 2346-2183
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia (Sede Medellín). Facultad de Minas.
institution Universidad Nacional de Colombia
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Torres-Madronero, Maria Constanza38ffd954-5232-45d3-9f43-254b88bef0f23002019-07-02T18:15:47Z2019-07-02T18:15:47Z2017-01-01ISSN: 2346-2183https://repositorio.unal.edu.co/handle/unal/60414http://bdigital.unal.edu.co/58746/Hyperspectral remote sensors acquire data coming from hundreds of narrow bands through the electromagnetic spectrum; this allows the terrestrial and maritime surfaces to be characterized for Earth observation. Hyperspectral image processing requires algorithms that combine spatial and spectral information. One way to take full advantage of spatial-spectral data within hyperspectral imagery is to use multiscale representations. A multiscale representation generates a family of images were fine details are systematically removed. This paper compares two multiscale representation approaches in order to improve the classification of hyperspectral imagery. The first approach is based on nonlinear diffusion, which obtains a multiscale representation by successive filtering. The second is based on binary partition tree, an approach inspired in region growing. The comparison is performed using a real hyperspectral image and a supper vector machine classifier. Both representation approaches allowed the classification of hyperspectral imagery to be improved. However, nonlinear diffusion results surpassed those obtained using binary partition tree.Los sensores remotos hiperespectrales adquieren datos a lo largo de cientos de bandas estrechas a través del espectro electromagnético permitiendo la caracterización de las superficies terrestres y marítimas para la observación de la Tierra. El procesamiento de imágenes hiperespectrales requiere de algoritmos que combinen la información espacial y espectral. Una forma de tomar ventaja de los datos espaciales y espectrales en las imágenes hiperespectrales es usar representaciones multiescala. Una representación multiescala genera una familia de imágenes donde los detalles finos son sistemáticamente removidos. Este artículo compara dos enfoques de representación multiescala para mejorar la clasificación de imágenes hiperespectrales. El primer enfoque se base en difusión no linear la cual obtiene la representación multiescala por medio de sucesivos filtros. El segundo se basa en un árbol de partición binaria, un enfoque inspirado en crecimiento de regiones. La comparación se realiza usando la imagen hiperespectral Indian Pines y un clasificador de máquinas de soporte vectorial. Ambos enfoques de representación permiten mejorar la clasificación de la imagen hiperespectral. Pero, los resultados de difusión no lineal superan los obtenidos usando el árbol de partición binaria.application/pdfspaUniversidad Nacional de Colombia (Sede Medellín). Facultad de Minas.https://revistas.unal.edu.co/index.php/dyna/article/view/50678Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaTorres-Madronero, Maria Constanza (2017) A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery. DYNA, 84 (200). pp. 129-134. ISSN 2346-218362 Ingeniería y operaciones afines / Engineeringdifusión no linealárbol de partición binariaclasificaciónimagen hiperespectralrepresentación multiescalapercepción remotanonlinear diffusionbinary partition treeclassificationhyperspectral imagerymultiscale representationremote sensingA comparative study of multiscale representations for spatialspectral classification of hyperspectral imageryArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL50678-320370-3-PB.pdfapplication/pdf727671https://repositorio.unal.edu.co/bitstream/unal/60414/1/50678-320370-3-PB.pdf276fd8063d56bc634c902bfcaf6ce98cMD51THUMBNAIL50678-320370-3-PB.pdf.jpg50678-320370-3-PB.pdf.jpgGenerated Thumbnailimage/jpeg9337https://repositorio.unal.edu.co/bitstream/unal/60414/2/50678-320370-3-PB.pdf.jpgd1a2145c111a9a75e6034f89dd8b0738MD52unal/60414oai:repositorio.unal.edu.co:unal/604142024-04-13 23:10:31.246Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co

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