A soft computing framework for image classification based on recurrence plots
Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself.
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Subjects: | F30 - Génétique et amélioration des plantes, K10 - Production forestière, télédétection, Eucalyptus, classification, logiciel, imagerie par satellite, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_2683, http://aims.fao.org/aos/agrovoc/c_1653, http://aims.fao.org/aos/agrovoc/c_24008, http://aims.fao.org/aos/agrovoc/c_36761, |
Online Access: | http://agritrop.cirad.fr/589936/ http://agritrop.cirad.fr/589936/1/2018Menini_SoftComputingFramework_GRSL.pdf |
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dig-cirad-fr-5899362024-01-29T05:43:18Z http://agritrop.cirad.fr/589936/ http://agritrop.cirad.fr/589936/ A soft computing framework for image classification based on recurrence plots. Menini Nathalia, Almeida Alexandre E., Lamparelli Rubens Augusto Camargo, Le Maire Guerric, dos Santos Jefersson A., Pedrini Helio, Hirota Marina, Torres Ricardo da S.. 2019. IEEE Geoscience and Remote Sensing Letters, 16 (2) : 320-324.https://doi.org/10.1109/LGRS.2018.2872132 <https://doi.org/10.1109/LGRS.2018.2872132> A soft computing framework for image classification based on recurrence plots Menini, Nathalia Almeida, Alexandre E. Lamparelli, Rubens Augusto Camargo Le Maire, Guerric dos Santos, Jefersson A. Pedrini, Helio Hirota, Marina Torres, Ricardo da S. eng 2019 IEEE Geoscience and Remote Sensing Letters F30 - Génétique et amélioration des plantes K10 - Production forestière télédétection Eucalyptus classification logiciel imagerie par satellite http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_2683 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24008 http://aims.fao.org/aos/agrovoc/c_36761 Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/589936/1/2018Menini_SoftComputingFramework_GRSL.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1109/LGRS.2018.2872132 10.1109/LGRS.2018.2872132 info:eu-repo/semantics/altIdentifier/doi/10.1109/LGRS.2018.2872132 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1109/LGRS.2018.2872132 |
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F30 - Génétique et amélioration des plantes K10 - Production forestière télédétection Eucalyptus classification logiciel imagerie par satellite http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_2683 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24008 http://aims.fao.org/aos/agrovoc/c_36761 F30 - Génétique et amélioration des plantes K10 - Production forestière télédétection Eucalyptus classification logiciel imagerie par satellite http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_2683 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24008 http://aims.fao.org/aos/agrovoc/c_36761 |
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F30 - Génétique et amélioration des plantes K10 - Production forestière télédétection Eucalyptus classification logiciel imagerie par satellite http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_2683 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24008 http://aims.fao.org/aos/agrovoc/c_36761 F30 - Génétique et amélioration des plantes K10 - Production forestière télédétection Eucalyptus classification logiciel imagerie par satellite http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_2683 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24008 http://aims.fao.org/aos/agrovoc/c_36761 Menini, Nathalia Almeida, Alexandre E. Lamparelli, Rubens Augusto Camargo Le Maire, Guerric dos Santos, Jefersson A. Pedrini, Helio Hirota, Marina Torres, Ricardo da S. A soft computing framework for image classification based on recurrence plots |
description |
Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself. |
format |
article |
topic_facet |
F30 - Génétique et amélioration des plantes K10 - Production forestière télédétection Eucalyptus classification logiciel imagerie par satellite http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_2683 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24008 http://aims.fao.org/aos/agrovoc/c_36761 |
author |
Menini, Nathalia Almeida, Alexandre E. Lamparelli, Rubens Augusto Camargo Le Maire, Guerric dos Santos, Jefersson A. Pedrini, Helio Hirota, Marina Torres, Ricardo da S. |
author_facet |
Menini, Nathalia Almeida, Alexandre E. Lamparelli, Rubens Augusto Camargo Le Maire, Guerric dos Santos, Jefersson A. Pedrini, Helio Hirota, Marina Torres, Ricardo da S. |
author_sort |
Menini, Nathalia |
title |
A soft computing framework for image classification based on recurrence plots |
title_short |
A soft computing framework for image classification based on recurrence plots |
title_full |
A soft computing framework for image classification based on recurrence plots |
title_fullStr |
A soft computing framework for image classification based on recurrence plots |
title_full_unstemmed |
A soft computing framework for image classification based on recurrence plots |
title_sort |
soft computing framework for image classification based on recurrence plots |
url |
http://agritrop.cirad.fr/589936/ http://agritrop.cirad.fr/589936/1/2018Menini_SoftComputingFramework_GRSL.pdf |
work_keys_str_mv |
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_version_ |
1792499592977186816 |