Fine-Tuning Contextual-Based Optimum-Path Forest for Land-Cover Classification

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Data

2016-05-01

Autores

Osaku, Daniel
Pereira, Danillo R. [UNESP]
Levada, Alexandre L. M.
Papa, Joao P. [UNESP]

Título da Revista

ISSN da Revista

Título de Volume

Editor

Ieee-inst Electrical Electronics Engineers Inc

Resumo

Contextual-based learning aims at considering neighboring pixels to improve pixelwise-oriented classification techniques. In this letter, we presented a metaheuristic framework for the optimization of nondiscrete Markovian models considering the optimum-path forest (OPF) classifier, and we proposed a post-processing procedure to avoid overcorrection over high-frequency regions. The proposed approach outperformed previous results obtained with standard OPF in satellite imagery.

Descrição

Palavras-chave

Contextual classification, optimum-path forest (OPF)

Como citar

Ieee Geoscience And Remote Sensing Letters. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 13, n. 5, p. 735-739, 2016.