Classificação do uso e cobertura da terra do Distrito Federal, Brasil a partir de dado de coerência interferométrica (Use and land cover classification of the Federal District, Brazil from interferometric coherence data)

Barbara Hass Miguel, Edson Eyji Sano

Resumo


O objetivo do presente trabalho foi avaliar o potencial das imagens de radar do satélite Sentinel 1A (banda C) para discriminar classes representativas de uso e cobertura da terra do Distrito Federal, Brasil. As imagens de coerência interferométrica utilizadas nesse estudo foram obtidas a partir de pares de imagens Single Look Complex (SLC) de junho e julho de 2018. Foi gerada uma composição colorida RGB a partir de imagens de coerência, intensidade de retroespalhamento e razão de retroespalhamento. Essa imagen foi classificada pelos métodos supervisionados Support Vector Machine (SVM) e Random Forest (RF). A validação temática foi realizada por matrizes de confusão, índice Kappa e exatidão global. Nesse contexto, foram investigadas cinco classes temáticas (água, área urbana, vegetação nativa, pastagem e agricultura). O classificador RF obteve melhor desempenho classificatório (Kappa= 0,68 e exatidão global = 79,1%) que o classificador SVM (Kappa= 0,64 e exatidão global = 75,7%). A coerência mostrou-se eficiente principalmente na identificação de corpos d’água e da área urbana. Os resultados foram satisfatórios para a classificação de uso e cobertura da terra do Distrito Federal, no entanto, houve confusão entre algumas classes e erros de comissão na classe área urbana em ambas as classificações.

 

 

A b s t r a c t

The aim of this study was to evaluate the potential of Sentinel 1A (C band) satellite radar images to discriminate representative classes of land use and land cover in the Federal District, Brazil. The interferometric coherence images used in this study were obtained from pairs of Single Look Complex images (SLC) of June and July of 2018. An RGB color composition was generated from images of coherence, backscattering intensity and backscattering ratio. This image was classified by the Supervised Methods Support Vector Machine (SVM) and Random Forest (RF). Thematic validation was performed by matrices of confusion, Kappa index and overall accuracy. In this context, five thematic classes (water, urban area, native vegetation, pasture and agriculture) were investigated. The RF classifier obtained a better classificatory performance (Kappa= 0.68 and overall accuracy = 79.1%) than the SVM classifier (Kappa= 0.64 and overall accuracy = 75.7%). The coherence was shown to be efficient mainly in the identification of water and the urban area. The results were satisfactory for the classification of use and land cover of the Federal District, however, there was confusion between some classes and errors of commission in the urban area class in both classifications.

Keywords: use and land cover, Interferometry, interferometric coherence, Sentinel 1A, radar, Remote Sensing


Palavras-chave


uso e cobertura da terra, Interferometria, coerência interferométrica, Sentinel 1A, radar, Sensoriamento Remoto.

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DOI: https://doi.org/10.26848/rbgf.v12.2.p427-442

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Revista Brasileira de Geografia Física - eISSN: 1984-2295

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