A Multirepresentational Fusion of Time Series for Pixelwise Classification
Danielle Dias, Allan Pinto, Ulisses Dias, Rubens Augusto Camargo Lamparelli, Guerric Le Maire, Ricardo da Silva Torres
Abstract
This article addresses the pixelwise classification problem based on temporal profiles, which are encoded in 2-D representations based on recurrence plots, Gramian angular/ difference fields, and Markov transition field. We propose a multirepresentational fusion scheme that exploits the complementary view provided by those time series representations and different data-driven feature extractors and classifiers. We validate our ensemble scheme in the problem related to the classification of eucalyptus plantations in remote sensing images. Achieved results demonstrate that our proposal overcomes recently proposed baselines, and now represents the new state-of-the-art classification solution for the target dataset.