Análise de metacaracterísticas para classificação de uso e cobertura do solo utilizando Random Forest

  • Roberto Paiva UFG
  • Sávio Oliveira UFG
  • Wellington Martins UFG
  • Leandro Parente UFG

Resumo


Este trabalho analisa o impacto do uso de metacaracterísticas geradas pelo algoritmo TWDTW para mapeamento do uso e cobertura do solo utilizando Random Forest. Os testes foram realizados classificando nove classes, para um conjunto de amostras da região do Mato Grosso, no Brasil. Nossos resultados mostram que as metacaracterísticas são promissoras para a melhora de acurácia, aumentando a acurácia global dos modelos testados. As melhoras mais significativas ocorrem na acurácia do produtor das classes de maior dificuldade de classificação. A importância das metacaracterísticas na classificação foi significativamente maior do que as características extraídas dos Índices EVI e NDVI e Bandas NIR e MIR.

Palavras-chave: Algoritmo TWDTW, mapeamento de uso e cobertura do solo, Random Forest

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Publicado
30/06/2020
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PAIVA, Roberto; OLIVEIRA, Sávio; MARTINS, Wellington; PARENTE, Leandro. Análise de metacaracterísticas para classificação de uso e cobertura do solo utilizando Random Forest. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 11. , 2020, Evento Online. Anais do XI Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Porto Alegre: Sociedade Brasileira de Computação, june 2020 . p. 71-80. ISSN 2595-6124. DOI: https://doi.org/10.5753/wcama.2020.11021.