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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

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Autor(es):
Faria, Fabio A. [1] ; Pedronette, Daniel C. G. [2] ; dos Santos, Jefersson A. [3] ; Rocha, Anderson [1] ; Torres, Ricardo da S. [1]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Sao Paulo - Brazil
[2] State Univ Sao Paulo UNESP, Dept Stat Appl Math & Comp, BR-13506900 Sao Paulo - Brazil
[3] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING; v. 7, n. 4, p. 1103-1115, APR 2014.
Citações Web of Science: 9
Resumo

In the past few years, segmentation and classification techniques have become a cornerstone of many successful remote sensing algorithms aiming at delineating geographic target objects. One common strategy relies on using multiple complex features to guide the delineation process with the objective of gathering complementary information for improving classification results. However, a persistent problem in this approach is how to combine different and noncorrelated feature descriptors automatically. In this regard, one solution is to combine them through multiple classifier systems (MCSs) in which the diversity of simple/non-complex classifiers is an essential issue in the definition of appropriate strategies for classifier fusion. In this paper, we propose a novel strategy for selecting classifiers (whereby a classifier is taken as a pair of learning method plus image descriptor) to be combined in MCS. In the proposed solution, diversity measures are used to assess the degree of agreement/disagreement between pairs of classifiers and ranked lists are created to sort them according to their diversity score. Thereafter, the classifiers are also sorted according to their performance through different evaluation measures (e. g., kappa and tau indices). In the end, a rank aggregation method is proposed to select the most suitable classifiers based on both the diversity and the effectiveness performance of classifiers. The proposed fusion framework has targeted at coffee crop classification and urban recognition but it is general enough to be used in a variety of other pattern recognition problems. Experimental results demonstrate that the novel strategy yields good results when compared to several baselines while using fewer classifiers and being much more efficient. (AU)

Processo FAPESP: 10/14910-0 - Métodos de fusão de evidências para recuperação e classificação multimídia
Beneficiário:Fabio Augusto Faria
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 13/08645-0 - Reclassificação e agregação de listas para tarefas de recuperação de imagens
Beneficiário:Daniel Carlos Guimarães Pedronette
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores
Processo FAPESP: 10/05647-4 - Computação forense e criminalística de documentos: coleta, organização, classificação e análise de evidências
Beneficiário:Anderson de Rezende Rocha
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores
Processo FAPESP: 12/18768-0 - Classificação multi-escala utilizando Floresta de Caminhos Ótimos
Beneficiário:Jefersson Alex dos Santos
Linha de fomento: Bolsas no Brasil - Pós-Doutorado