دانلود مقاله انگلیسی رایگان:DuPLO: نقطه دید DUal معماری یادگیری عمیق برای classificati سری زمانی - 2019
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  • DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
    DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn


    ترجمه فارسی عنوان مقاله:

    DuPLO: نقطه دید DUal معماری یادگیری عمیق برای classificati سری زمانی


    منبع:

    Sciencedirect - Elsevier - ISPRS Journal of Photogrammetry and Remote Sensing, 149 (2019) 91-104: doi:10:1016/j:isprsjprs:2019:01:011


    نویسنده:

    Roberto Interdonatoa,⁎, Dino Iencob, Raffaele Gaetanoa, Kenji Osec


    چکیده انگلیسی:

    Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10 m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation, unfortunately, most of machine learning approaches commonly leveraged in remote sensing field fail to take advantage of spatio-temporal dependencies present in such data. Recently, new generation deep learning methods allowed to significantly advance research in this field. These approaches have generally focused on a single type of neural network, i.e., Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), which model different but complementary information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs). In this work, we propose the first deep learning architecture for the analysis of SITS data, namely DuPLO (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity. Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination of both models would produce a more diverse and complete representation of the information for the underlying land cover classification task. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Gard site in Mainland France and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal
    Keywords: Satellite image time series | Deep learning | Land cover classification | Sentinel-2


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 14
    حجم فایل: 5987 کیلوبایت

    قیمت: رایگان


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