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Multiscale Classification By Using Optimum Path-Forest

Grant number: 12/18768-0
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): April 01, 2013
Effective date (End): August 31, 2013
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Ricardo da Silva Torres
Grantee:Jefersson Alex dos Santos
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Nowadays, the main problems in region recognition of remote sensing images are: (1) the dependence of the classification methodson the segmentation quality; and (2) the selection of representativesamples for training. The major challenge is that the samplesindicated by the user are not always enough to define the best segmentation scale.Furthermore, the indication of samples can be costly, since it often requires to visit studied places in loco.The objective of this research project is to develop aninteractive multiscale classification approach that allows segmentation andclassification refinement according to user indications. The segmentation-dependence problem will be addressed by using techniques that rely on multiple scales instead ofonly one segmentation result. The selection of representative samples, in turn, willbe supported by the development of new approaches based on active learning with user interactions.The proposed method will be validated in three applications associated withdistinct research areas found at Institute of Computing, Unicamp: (1) phenologicalpattern recongnition; (2) agricultural region classification by usingmultisensor data; and (3) illegal region identification in aerial images.

Scientific publications (5)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SAITO, PRISCILA T. M.; NAKAMURA, RODRIGO Y. M.; AMORIM, WILLIAN P.; PAPA, JOAO P.; DE REZENDE, PEDRO J.; FALCAO, ALEXANDRE X. Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint. PLoS One, v. 10, n. 6 JUN 26 2015. Web of Science Citations: 3.
DOS SANTOS, JEFERSSON A.; PENATTI, OTAVIO A. B.; GOSSELIN, PHILIPPE-HENRI; FALCAO, ALEXANDRE X.; PHILIPP-FOLIGUET, SYLVIE; TORRES, RICARDO DA S. Efficient and Effective Hierarchical Feature Propagation. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, v. 7, n. 12, SI, p. 4632-4643, DEC 2014. Web of Science Citations: 10.
FARIA, FABIO A.; DOS SANTOS, JEFERSSON A.; ROCHA, ANDERSON; TORRES, RICARDO DA S. A framework for selection and fusion of pattern classifiers in multimedia recognition. PATTERN RECOGNITION LETTERS, v. 39, n. SI, p. 52-64, APR 1 2014. Web of Science Citations: 24.
FARIA, FABIO A.; PEDRONETTE, DANIEL C. G.; DOS SANTOS, JEFERSSON A.; ROCHA, ANDERSON; TORRES, RICARDO DA S. Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, v. 7, n. 4, p. 1103-1115, APR 2014. Web of Science Citations: 9.
NAKAMURA, RODRIGO Y. M.; GARCIA FONSECA, LEILA MARIA; DOS SANTOS, JEFERSSON ALEX; TORRES, RICARDO DA S.; YANG, XIN-SHE; PAPA, JOAO PAPA. Nature-Inspired Framework for Hyperspectral Band Selection. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, v. 52, n. 4, p. 2126-2137, APR 2014. Web of Science Citations: 29.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.