Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Year: 2017
Type: article
Abstract: Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.
Authors: Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, École Polytechnique Fédérale de Lausanne, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Natural Science Foundation of China, Chinese Academy of Sciences, Hunan Normal University, Albert-Ludwigs-Universität Freiburg, Deutsches Zentrum für Luft- und Raumfahrt, China Scholarship Council, Centre National de la Recherche Scientifique, Technische Universität München, Université de Lausanne, Universität Zürich, Wuhan University, Xi'an University of Posts and Telecommunications, University of Chinese Academy of Sciences, Wageningen University and Research, Xi'an Institute of Optics and Precision Mechanics, Wageningen University Fund
OpenAlex ID: W2764034829