A framework for remote sensing images processing using deep learning technique

TL;DR

This paper introduces a flexible, efficient framework combining open-source tools for applying deep learning to remote sensing images, facilitating user-level access and operation on large datasets.

Contribution

It presents a novel framework integrating Orfeo Toolbox and TensorFlow for deep learning on remote sensing data, supporting large images and diverse hardware.

Findings

Supports large-scale remote sensing image processing

Ensures computational efficiency across hardware configurations

Provides a user-friendly, operational deep learning solution

Abstract

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.