Atualização de Documento

XIX SBSR

Formulário de atualização

O sistema de atualização, para eventuais correções, estará ativo até 30/11/2019.

Contato: bibdigital@inpe.br


Nome do Campo		Valor do Campo
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	Não esqueça de clicar na opção "+" para informar os dados dos co-autores, se tiver.		v
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Resumo	(*)(?)	The culture of sugarcane has great importance in the brazilian agribusiness. Remote sensing images have been tradicionally used on manual mapping of sugarcane fields. Manual classification is a laborious and time-consuming task, especially given the size of the territory, and it is still necessary to assess the quality of the maps. Image fusion can improve the identification and mapping of surface features. The computational data mining methodology demonstrates high potential for application in areas related to crop mapping and several classification techniques can be used. Most studies on fusion of remote sensing images have focused on the analysis of spectral and spatial quality of the products obtained by different algorithms, however, once classification is applied on these products, it is important to analyze the impact of fusion in the classification. In the literature there are few studies on this topic, especially considering the Landsat-8. In this context, we evaluated five pansharpening methods - Intensity-Hue-Saturation (IHS), Principal Components (PC), Gran-Schmidt (GS), Discrete Wavelet Transform (DWT) and DWT+IHS for the classification of sugarcane fields in a Landsat-8 image (bands 4, 5 and 6). The Support Vector Machine (SVM) algorithm was used to perform a target detection of sugarcane, using a binary classification. The samples used were selected based on a field survey realized on the study area. The best fusion techniques were the DWT+IHS, DWT and IHS, which obtained higher Universal Image Quality Index (UIQI) and Spatial Relative Dimensionless Global Error in Synthesis (SERGAS) values. However, considering the effects on classification, the GS fusion showed better results than other methods.
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