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Image-Based Time Series Representations for Pixelwise Eucalyptus Region Classification: A Comparative Study

Abstract : Pixelwise image classification based on time series profiles has been very effective in several applications. In this letter, we investigate recently proposed image-based time series encoding approaches [e.g., Gramian angular summation field/Gramian angular difference field (GASF/GADF) and Markov transition field (MTF)] to support the identification of eucalyptus regions in remote sensing images. We perform a comparative study concerning the combination of image-based representations suitable for encoding the most important time series patterns with the ability of state-of-the-art deep-learning-based approaches for characterizing image visual properties. The comparative study demonstrates that the evaluated image representations, combined with different deep learning feature extractors lead to highly effective classification results, which are superior to those of recently proposed methods for time-series-based eucalyptus plantation detection.
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https://hal.inrae.fr/hal-02912924
Contributor : Dominique Fournier <>
Submitted on : Friday, August 7, 2020 - 9:28:02 AM
Last modification on : Wednesday, February 24, 2021 - 3:31:10 AM

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Danielle Dias, Ulisses Dias, Nathalia Menini, Rubens Lamparelli, Guerric Le Maire, et al.. Image-Based Time Series Representations for Pixelwise Eucalyptus Region Classification: A Comparative Study. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2020, 17 (8), pp.1450-1454. ⟨10.1109/LGRS.2019.2946951⟩. ⟨hal-02912924⟩

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