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A Multi-Representational Fusion of Time Series for Pixelwise Classification

Abstract : This paper addresses the pixelwise classification problem based on temporal profiles, which are encoded in two-dimensional representations based on Recurrence Plots, Gramian Angular/Difference Fields, and Markov Transition Field. We propose a multi-representational 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.
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Contributor : Dominique Fournier <>
Submitted on : Wednesday, September 16, 2020 - 9:09:55 AM
Last modification on : Monday, June 7, 2021 - 4:16:09 PM


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Danielle Dias, Allan Pinto, Ulisses Dias, Rubens Lamparelli, Guerric Le Maire, et al.. A Multi-Representational Fusion of Time Series for Pixelwise Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2020, 13, pp.4399-4409. ⟨10.1109/JSTARS.2020.3012117⟩. ⟨hal-02940070⟩



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