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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A Soft Computing Framework for Image Classification Based on Recurrence Plots

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Author(s):
Menini, Nathalia [1] ; Almeida, Alexandre E. [1] ; Lamparelli, Rubens [2] ; le Maire, Guerric [3] ; dos Santos, Jefersson A. [4] ; Pedrini, Helio [1] ; Hirota, Marina [5, 6] ; Torres, Ricardo da S. [1]
Total Authors: 8
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, BR-13083872 Campinas, SP - Brazil
[2] Univ Estadual Campinas, Nucleo Interdisciplinar Planejamento Energet, BR-13083896 Campinas, SP - Brazil
[3] Univ Montpellier, CIRAD, UMR Eco & Sols, INRA, IRD, Montpellier SupAgro, F-75116 Montpellier - France
[4] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG - Brazil
[5] Univ Estadual Campinas, Inst Biol, BR-13083862 Campinas, SP - Brazil
[6] Univ Fed Santa Catarina, Dept Phys, BR-88036000 Florianopolis, SC - Brazil
Total Affiliations: 6
Document type: Journal article
Source: IEEE Geoscience and Remote Sensing Letters; v. 16, n. 2, p. 320-324, FEB 2019.
Web of Science Citations: 0
Abstract

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. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support type: Research Projects - Thematic Grants
FAPESP's process: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
Grantee:Rubens Augusto Camargo Lamparelli
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Grantee:Ricardo da Silva Torres
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 15/02105-0 - Identifying temporal changes in tropical South American vegetation: a breakpoint detection approach
Grantee:Alexandre Esteves Almeida
Support type: Scholarships in Brazil - Master
FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
Grantee:Anderson de Rezende Rocha
Support type: Research Projects - Thematic Grants
FAPESP's process: 16/26170-8 - Structural breaks detection in time series and its use in the definition of stability measures
Grantee:Nathália Menini Cardoso dos Santos
Support type: Scholarships in Brazil - Master
FAPESP's process: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
Grantee:Leonor Patricia Cerdeira Morellato
Support type: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)
FAPESP's process: 18/06918-3 - Quantifying Amazon's resilience through structural breaks associated with extreme climatic events
Grantee:Nathália Menini Cardoso dos Santos
Support type: Scholarships abroad - Research Internship - Master's degree