A framework for remote sensing images processing using deep learning technique
This provides a practical solution for the remote sensing community, though it is incremental as it combines existing libraries.
The authors tackled the lack of operational deep learning tools for remote sensing by developing a framework that integrates TensorFlow with Orfeo ToolBox, enabling efficient processing of large images across hardware configurations.
Deep learning techniques are becoming increasingly important to solve a number of image processing tasks. Among common algorithms, Convolutional Neural Networks and Recurrent Neural Networks 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 user-level for the remote sensing community. In this paper, we presents a framework enabling the use of deep learning techniques with remote sensing images and geospatial data. Our solution takes roots in two extensively used open-source libraries, the remote sensing image processing library Orfeo ToolBox, and the high performance numerical computation library TensorFlow. It can apply deep nets without restriction on images size and is computationally efficient, regardless hardware configuration.