Deep Learning in Remote Sensing

被引：9

作者：

Zhu, Xiao Xiang ^{[1
,2
,3
,4
,5
,6
]}

Tuia, Devis ^{[7
,8
,9
,10
,11
]}

Mou, Lichao ^{[2
,6
,12
]}

Xia, Gui-Song ^{[13
,14
]}

Zhang, Liangpei ^{[15
]}

Xu, Feng ^{[16
,17
]}

Fraundorfer, Friedrich ^{[18
,19
,20
,21
,22
]}

机构：

[1] TUM, Signal Proc Earth Observat, Munich, Germany

[2] German Aerosp Ctr DLR, Cologne, Germany

[3] DLR, Remote Sensing Technol Inst, Team Signal Anal, Cologne, Germany

[4] Helmholtz Young Investigator Grp SiPEO, Munich, Germany

[5] DLR, Cologne, Germany

[6] TUM, Munich, Germany

[7] Univ Valencia, Valencia, Spain

[8] Univ Colorado, Boulder, CO 80309 USA

[9] Ecole Polytech Fed Lausanne, Lausanne, Switzerland

[10] Wageningen Univ, GeoInformat Sci & Remote Sensing Lab, Wageningen, Netherlands

[11] Univ Zurich, Zurich, Switzerland

[12] Univ Freiburg, Comp Vis Grp, Freiburg, Germany

[13] Wuhan Univ, Key Lab Informat Engn Surveying Mapping & Remote, Wuhan, Hubei, Peoples R China

[14] Paris Dauphine Univ, CNRS, Ctr Rech Math Decis, Paris, France

[15] Wuhan Univ, Wuhan, Hubei, Peoples R China

[16] Sch Informat Sci & Technol, Hefei, Anhui, Peoples R China

[17] Key Lab for Informat Sci Electromagnet Waves, Shanghai, Peoples R China

[18] Graz Univ Technol, Graz, Austria

[19] Univ Kentucky, Lexington, KY 40506 USA

[20] Univ N Carolina, Chapel Hill, NC USA

[21] Swiss Fed Inst Technol, Zurich, Switzerland

[22] Tech Univ Munich, Fac Civil Geo & Environm Engn, Munich, Germany

来源：

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE | 2017年 / 5卷 / 04期

基金：

瑞士国家科学基金会; 中国国家自然科学基金; 欧洲研究理事会;

关键词：

CONVOLUTIONAL NEURAL-NETWORK; OBJECT DETECTION; HIGH-RESOLUTION; DATA-FUSION; SCENE CLASSIFICATION; VEHICLE DETECTION; IMAGE RETRIEVAL; SAR ATR; REPRESENTATION; FEATURES;

D O I：

10.1109/MGRS.2017.2762307

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

引用

页码：8 / 36

页数：29

共 50 条

[1] Deep Learning for Urban Remote Sensing
Audebert, Nicolas
Boulch, Alexandre
Randrianarivo, Hicham
Le Saux, Bertrand
Ferecatu, Marin
Lefevre, Sebastien
Marlet, Renaud
[J]. 2017 JOINT URBAN REMOTE SENSING EVENT (JURSE), 2017,
[2] Deep Learning for Remote Sensing Applications
Lee, Moung-Jin
Lee, Won-Jin
Lee, Seung-Kuk
Jung, Hyung-Sup
[J]. KOREAN JOURNAL OF REMOTE SENSING, 2022, 38 (06) : 1581 - 1587
[3] Deep Learning for Remote Sensing Image Understanding
Zhang, Liangpei
Xia, Gui-Song
Wu, Tianfu
Lin, Liang
Tai, Xue Cheng
[J]. JOURNAL OF SENSORS, 2016, 2016
[4] Deep Learning and Remote Sensing Data Analysis
Zhang L.
Li Y.
Hou Z.
Li X.
Geng H.
Wang Y.
Li J.
Zhu P.
Mei J.
Jiang Y.
Li S.
Xin Q.
Cui Y.
Liu S.
[J]. 1857, Editorial Board of Medical Journal of Wuhan University (45): : 1857 - 1864
[5] Compressed Remote Sensing by Using Deep Learning
Mirrashid, Alireza
Beheshti, Ali Asghar
[J]. 2018 9TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATIONS (IST), 2018, : 549 - 552
[6] REMOTE SENSING AND DEEP LEARNING FOR SUSTAINABLE MINING
Ghamisi, Pedram
Li, Hao
Jackisch, Robert
Rasti, Behnood
Gloaguen, Richard
[J]. IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 3739 - 3742
[7] A review on deep learning in UAV remote sensing
Osco, Lucas Prado
Marcato Junior, Jose
Marques Ramos, Ana Paula
de Castro Jorge, Lucio Andre
Fatholahi, Sarah Narges
Silva, Jonathan de Andrade
Matsubara, Edson Takashi
Pistori, Hemerson
Goncalves, Wesley Nunes
Li, Jonathan
[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 102
[8] Feature and Deep Learning in Remote Sensing Applications
Ball, John E.
Anderson, Derek T.
Chan, Chee Seng
[J]. JOURNAL OF APPLIED REMOTE SENSING, 2018, 11
[9] Advanced Machine Learning and Deep Learning Approaches for Remote Sensing
Jeon, Gwanggil
[J]. REMOTE SENSING, 2023, 15 (11)
[10] Deep Hash Learning for Remote Sensing Image Retrieval
Liu, Chao
Ma, Jingjing
Tang, Xu
Liu, Fang
Zhang, Xiangrong
Jiao, Licheng
[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (04): : 3420 - 3443

← 1 2 3 4 5 →