Deep Neural Networks for automatic extraction of features in time series satellite images

• URL: http://arxiv.org/abs/2008.08432v1
• Date: Mon, 17 Aug 2020 09:26:52 GMT
• Title: Deep Neural Networks for automatic extraction of features in time series satellite images
• Authors: Gael Kamdem De Teyou, Yuliya Tarabalka, Isabelle Manighetti, Rafael Almar, Sebastien Tripod
• Abstract summary: We exploit both temporal and spatial information provided by Landsat Sentinel, SPOT, and other time series images to generate land cover maps. Experimental results show that the Pleiades temporal information allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth.
• Score: 3.3598755777055374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. In this work, we 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.

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