Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

被引:0
|
作者
Zhu X.X. [1 ]
Tuia D. [2 ]
Mou L. [3 ]
Xia G.-S. [4 ]
Zhang L. [4 ]
Xu F. [5 ]
Fraundorfer F. [6 ]
机构
[1] GeoInformation Science and Remote Sensing Laboratory, Wageningen University
[2] German Aerospace Center (DLR), Technical University of Munich
[3] State Key Laboratory of Information Engineering, Surveying, Mapping, and Remote Sensing, Wuhan University
[4] University of Kentucky, University of North Carolina, Lexington, CH
来源
| 1600年 / Institute of Electrical and Electronics Engineers Inc.卷 / 05期
基金
中国国家自然科学基金; 欧洲研究理事会; 美国国家科学基金会; 欧盟地平线“2020”;
关键词
197;
D O I
10.1109/MGRS.2017.2762307
中图分类号
学科分类号
摘要
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. © 2013 IEEE.
引用
收藏
页码:8 / 36
页数:28
相关论文
共 50 条
  • [21] Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
    Ball, John E.
    Anderson, Derek T.
    Chan, Chee Seng
    JOURNAL OF APPLIED REMOTE SENSING, 2017, 11
  • [22] Deep learning techniques for remote sensing image scene classification: A comprehensive review, current challenges, and future directions
    Kumari, Monika
    Kaul, Ajay
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (22):
  • [23] Deep Learning Models for Hazard-Damaged Building Detection Using Remote Sensing Datasets: A Comprehensive Review
    Wang, Lili
    Wu, Jidong
    Yang, Youtian
    Tang, Rumei
    Ya, Ru
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 15301 - 15318
  • [24] Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications - A comprehensive review
    Khlifi, Manel Khazri
    Boulila, Wadii
    Farah, Imed Riadh
    COMPUTER SCIENCE REVIEW, 2023, 50
  • [25] Beyond Supervised Learning in Remote Sensing: A Systematic Review of Deep Learning Approaches
    Hosseiny, Benyamin
    Mahdianpari, Masoud
    Hemati, Mohammadali
    Radman, Ali
    Mohammadimanesh, Fariba
    Chanussot, Jocelyn
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 1035 - 1052
  • [26] A review of deep learning methods for semantic segmentation of remote sensing imagery
    Yuan, Xiaohui
    Shi, Jianfang
    Gu, Lichuan
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 169
  • [27] Remote Sensing Object Detection in the Deep Learning Era-A Review
    Gui, Shengxi
    Song, Shuang
    Qin, Rongjun
    Tang, Yang
    REMOTE SENSING, 2024, 16 (02)
  • [28] Deep learning algorithms for hyperspectral remote sensing classifications: an applied review
    Pal, Mahesh
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2024, 45 (02) : 451 - 491
  • [29] Deep learning in remote sensing applications: A meta-analysis and review
    Ma, Lei
    Liu, Yu
    Zhang, Xueliang
    Ye, Yuanxin
    Yin, Gaofei
    Johnson, Brian Alan
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 152 : 166 - 177
  • [30] A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology
    Kadhim, Israa
    Abed, Fanar M. M.
    SENSORS, 2023, 23 (06)