Case studies of classification of cultivated areas with coffee by texture descriptors

Lucas Silva da Silveira, Domingos Sárvio Magalhães Valente, Francisco de Assis de Carvalho Pinto, Fábio Lúcio Santos

Abstract


The objective of this work is to develop a system to identify areas cultivated with coffee using ANNs having as input variables descriptors Haralick. We used the training algorithm Back-propagation and Levenberg -Marquardt method. There were two cases of study: in the first step, the ANN was trained with representative samples of each class of interest (coffee, forest, water, bare soil, and urban area), thus verifying the potential to discriminate output classes; in the second step the objective was to classify the coffee plantations accordingly with the age. For the evaluation of the classification performance of ANNs was employed a reference map and land use through the Geographic Information System. The concordance between the thematic maps, classified by ANN, and the reference map was evaluated by Kappa index. It was verified that Kappa index for discriminating the coffee region of the other class of interest was 0,652 in the first case study, performance as very good. To classify the coffee plantations accordingly with the age, Kappa index was variable (0.675 to 0.4783), very good for Itatiaia farm and reasonable to Pedra Redonda farm.

Keywords


Artificial neural networks; remote sensing; supervised classification

References


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DOI: http://dx.doi.org/10.25186/cs.v11i4.1155

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