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Deep Neural Networks for Automatic Extraction of Features in Time Series Optical Satellite Images

Abstract : Many Earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. These time series are a great opportunity to detect and measure the space and time changes of anthropogenic and natural features. In this work, we thus exploit both temporal and spatial information provided by these images to generate land cover maps. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are provided. Experimental results show that the temporal information provided by time series images allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth in both time and space.
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https://hal-insu.archives-ouvertes.fr/insu-03688506
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Submitted on : Saturday, June 4, 2022 - 5:48:38 PM
Last modification on : Sunday, June 5, 2022 - 3:38:42 AM

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G. Kamdem de Teyou, y. Tarabalka, I. Manighetti, R. Almar, S. Tripodi. Deep Neural Networks for Automatic Extraction of Features in Time Series Optical Satellite Images. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 43B2, pp.1529-1535. ⟨10.5194/isprs-archives-XLIII-B2-2020-1529-2020⟩. ⟨insu-03688506⟩

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