Fine-Tuning Contextual-Based Optimum-Path Forest for Land-Cover Classification
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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/165152 |
| Acceso en línea: | http://dx.doi.org/10.1109/LGRS.2016.2541458 http://hdl.handle.net/11449/165152 |
| Access Level: | acceso abierto |
| Palabra clave: | Contextual classification optimum-path forest (OPF) |
| Sumario: | 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. |
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