Maximum discrimination index: a tool for land cover identification
- Autores
- Lencina, Alberto; Weber, Christian
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- This work presents an adaptable index that is applied to a pair of covers to be discriminated. Its adaptability relies on the procedure to determine the numerical value of the wavelengths or bands involved: the maximization of an operator based on the geometric mean of squared differences. This index is applied to the particular case of discrimination of wheat from ryegrass in different phenological stages. The maximum discrimination index outperforms other indices such as the normalized difference vegetation index, advanced normalized vegetation index and normalized difference greenness index. Its efficacy of discrimination is characterized and compared with the normalized difference greenness index (the second with better performance). It is observed that the proposed index has a more predictable behavior and reaches a discrimination accuracy as high as 95.5%. The maximum discrimination index could be adjusted to different covers and employed as a tool for discrimination. Spectral signatures coming from any platform: field, aerial or satellite, can be handled.
Facultad de Ciencias Agrarias y Forestales
Centro de Investigaciones Ópticas - Materia
-
Ciencias Agrarias
Discrimination
Ryegrass
Spectral signature
Vegetation index
Wheat - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/146060
Ver los metadatos del registro completo
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Maximum discrimination index: a tool for land cover identificationLencina, AlbertoWeber, ChristianCiencias AgrariasDiscriminationRyegrassSpectral signatureVegetation indexWheatThis work presents an adaptable index that is applied to a pair of covers to be discriminated. Its adaptability relies on the procedure to determine the numerical value of the wavelengths or bands involved: the maximization of an operator based on the geometric mean of squared differences. This index is applied to the particular case of discrimination of wheat from ryegrass in different phenological stages. The maximum discrimination index outperforms other indices such as the normalized difference vegetation index, advanced normalized vegetation index and normalized difference greenness index. Its efficacy of discrimination is characterized and compared with the normalized difference greenness index (the second with better performance). It is observed that the proposed index has a more predictable behavior and reaches a discrimination accuracy as high as 95.5%. The maximum discrimination index could be adjusted to different covers and employed as a tool for discrimination. Spectral signatures coming from any platform: field, aerial or satellite, can be handled.Facultad de Ciencias Agrarias y ForestalesCentro de Investigaciones Ópticas2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf1113-1122http://sedici.unlp.edu.ar/handle/10915/146060enginfo:eu-repo/semantics/altIdentifier/issn/1735-1472info:eu-repo/semantics/altIdentifier/issn/1735-2630info:eu-repo/semantics/altIdentifier/doi/10.1007/s13762-019-02547-5info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:24:14Zoai:sedici.unlp.edu.ar:10915/146060Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:24:14.916SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Maximum discrimination index: a tool for land cover identification |
title |
Maximum discrimination index: a tool for land cover identification |
spellingShingle |
Maximum discrimination index: a tool for land cover identification Lencina, Alberto Ciencias Agrarias Discrimination Ryegrass Spectral signature Vegetation index Wheat |
title_short |
Maximum discrimination index: a tool for land cover identification |
title_full |
Maximum discrimination index: a tool for land cover identification |
title_fullStr |
Maximum discrimination index: a tool for land cover identification |
title_full_unstemmed |
Maximum discrimination index: a tool for land cover identification |
title_sort |
Maximum discrimination index: a tool for land cover identification |
dc.creator.none.fl_str_mv |
Lencina, Alberto Weber, Christian |
author |
Lencina, Alberto |
author_facet |
Lencina, Alberto Weber, Christian |
author_role |
author |
author2 |
Weber, Christian |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Agrarias Discrimination Ryegrass Spectral signature Vegetation index Wheat |
topic |
Ciencias Agrarias Discrimination Ryegrass Spectral signature Vegetation index Wheat |
dc.description.none.fl_txt_mv |
This work presents an adaptable index that is applied to a pair of covers to be discriminated. Its adaptability relies on the procedure to determine the numerical value of the wavelengths or bands involved: the maximization of an operator based on the geometric mean of squared differences. This index is applied to the particular case of discrimination of wheat from ryegrass in different phenological stages. The maximum discrimination index outperforms other indices such as the normalized difference vegetation index, advanced normalized vegetation index and normalized difference greenness index. Its efficacy of discrimination is characterized and compared with the normalized difference greenness index (the second with better performance). It is observed that the proposed index has a more predictable behavior and reaches a discrimination accuracy as high as 95.5%. The maximum discrimination index could be adjusted to different covers and employed as a tool for discrimination. Spectral signatures coming from any platform: field, aerial or satellite, can be handled. Facultad de Ciencias Agrarias y Forestales Centro de Investigaciones Ópticas |
description |
This work presents an adaptable index that is applied to a pair of covers to be discriminated. Its adaptability relies on the procedure to determine the numerical value of the wavelengths or bands involved: the maximization of an operator based on the geometric mean of squared differences. This index is applied to the particular case of discrimination of wheat from ryegrass in different phenological stages. The maximum discrimination index outperforms other indices such as the normalized difference vegetation index, advanced normalized vegetation index and normalized difference greenness index. Its efficacy of discrimination is characterized and compared with the normalized difference greenness index (the second with better performance). It is observed that the proposed index has a more predictable behavior and reaches a discrimination accuracy as high as 95.5%. The maximum discrimination index could be adjusted to different covers and employed as a tool for discrimination. Spectral signatures coming from any platform: field, aerial or satellite, can be handled. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/146060 |
url |
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dc.language.none.fl_str_mv |
eng |
language |
eng |
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dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf 1113-1122 |
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