Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

John E. Ball; Derek T. Anderson; Chee Seng Chan Sr.

doi:10.1117/1.JRS.11.042609

23 September 2017 Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

John E. Ball, Derek T. Anderson, Chee Seng Chan Sr.

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 042609 (September 2017). https://doi.org/10.1117/1.JRS.11.042609

Abstract

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

John E. Ball, Derek T. Anderson, and Chee Seng Chan Sr. "Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community," Journal of Applied Remote Sensing 11(4), 042609 (23 September 2017). https://doi.org/10.1117/1.JRS.11.042609

Received: 3 June 2017; Accepted: 16 August 2017; Published: 23 September 2017

ACCESS THE FULL ARTICLE

JOURNAL ARTICLE
54 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY