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Using Fuzzy Multilayer Perceptrons for the Classification of Time Series

  • Toni Pimentel
  • Fernando M. Ramos
  • Sandra Sandri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8256)

Abstract

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%.

Keywords

Membership Function Vegetation Index Fuzzy Inference System Input Pattern Moderate Resolution Image Spectroradiometer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Toni Pimentel
    • 1
  • Fernando M. Ramos
    • 1
  • Sandra Sandri
    • 1
  1. 1.Instituto Nacional de Pesquisas EspaciaisSão José dos CamposBrazil

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