A Soft Computing Framework for Image Classification Based on Recurrence Plots

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]

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

• Document type: Journal article
• Source: IEEE Geoscience and Remote Sensing Letters; v. 16, n. 2, p. 320-324, FEB 2019.
• Web of Science Citations: 1

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: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
• Grantee: Rubens Augusto Camargo Lamparelli
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• 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 Opportunities: Scholarships in Brazil - Master
• 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 Opportunities: Scholarships abroad - Research Internship - Master's degree
• FAPESP's process: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
• Grantee: Leonor Patricia Cerdeira Morellato
• Support Opportunities: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)
• 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 Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/02105-0 - Identifying temporal changes in tropical South American vegetation: a breakpoint detection approach
• Grantee: Alexandre Esteves Almeida
• Support Opportunities: 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 Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
• Grantee: Ricardo da Silva Torres
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE