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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.)

Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions

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Author(s):
Almeida, Alexandre E. ; Torres, Ricardo da S.
Total Authors: 2
Document type: Journal article
Source: IEEE Geoscience and Remote Sensing Letters; v. 14, n. 9, p. 1499-1503, SEP 2017.
Web of Science Citations: 3
Abstract

In several applications, the automatic identification of regions of interest in remote sensing images is based on the assessment of the similarity of associated time series, i.e., two regions are considered as belonging to the same class if the patterns found in their spectral information observed over time are somewhat similar. In this letter, we investigate the use of a genetic programming (GP) framework to discover an effective combination of time series similarity functions to be used in remote sensing classification tasks. Performed experiments in a Forest-Savanna classification scenario demonstrated that the GP framework yields effective results when compared with the use of traditional widely used similarity functions in isolation. (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: 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: 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: 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: 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