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A framework for remote sensing images processing using deep learning techniques

Abstract : Deep learning (DL) techniques are becoming increasingly important to solve a number of image processing tasks. Among common algorithms, convolutional neural network- and recurrent neural network-based systems achieve state-of-the-art results on satellite and aerial imagery in many applications. While these approaches are subject to scientific interest, there is currently no operational and generic implementation available at the user level for the remote sensing (RS) community. In this letter, we present a framework enabling the use of DL techniques with RS images and geospatial data. Our solution takes roots in two extensively used open-source libraries, the RS image processing library Orfeo ToolBox and the high-performance numerical computation library TensorFlow. It can apply deep nets without restriction on image size and is computationally efficient, regardless of hardware configuration.
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Romain Cresson. A framework for remote sensing images processing using deep learning techniques. IEEE Geoscience and Remote Sensing Letters, 2019, 16 (1), pp.25-29. ⟨10.1109/LGRS.2018.2867949⟩. ⟨hal-02607952⟩

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