Using Fuzzy Multilayer Perceptrons for the Classification of Time Series
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In the last decades, rainforests all over the world have been subjected to high rates of land use change due to deforestation. Tracking and understanding the trends and patterns of these changes is crucial for the creation and implementation of effective policies for sustainable development and environment protection. Here we propose the use of Fuzzy Multilayer Perceptrons (Fuzzy MLP) for classification of land use and land cover patterns in the Brazilian Amazon, using time series of vegetation index, taken from NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Results show that the combination of degree of ambiguity and fuzzy desired output, two of the Fuzzy MLP techniques implemented here, provides an overall accuracy ranging from 89% to 96%.
KeywordsMembership Function Vegetation Index Fuzzy Inference System Input Pattern Moderate Resolution Image Spectroradiometer
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