TU2.R13.4

LABEL NOISE ROBUST IMAGE REPRESENTATION LEARNING BASED ON SUPERVISED VARIATIONAL AUTOENCODERS IN REMOTE SENSING

Gencer Sumbul, Begüm Demir, Technische Universität Berlin, Germany
Session:
TU2.R13: Representation Learning in Remote Sensing II: Time Series, Change Detection, and Label Noise Oral
Track:
Community-Contributed Sessions
Location:
Room 105
Presentation Time:
Tue, 18 Jul, 10:51 - 11:03 Pacific Time (UTC -7)
Session Co-Chairs:
Marc Rußwurm, Ecole Polytechnique Federale de Lausanne and Begüm Demir, Technische Universität Berlin
Session Manager:
Ge Jiang
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Session TU2.R13
TU2.R13.1: SELF-SUPERVISED SPATIO-TEMPORAL REPRESENTATION LEARNING OF SATELLITE IMAGE TIME SERIES
Iris Dumeur, Silvia Valero, Jordi Inglada, CESBIO, Université de Toulouse, CNES/CNRS/INRAe/IRD/UPS, France
TU2.R13.2: TEMPORALMIX FOR SELF-SUPERVISED LEARNING ON SATELLITE IMAGE TIME SERIES
Jules Bourcier, Preligens / Inria, France; Gohar Dashyan, Preligens, France; Karteek Alahari, Jocelyn Chanussot, Inria, France
TU2.R13.3: EXPLORATIONS IN SELF-SUPERVISED LEARNING FOR CHANGE DETECTION
Srikumar Sastry, Aayush Dhakal, Washington University in St. Louis, United States; Benjamin Brodie, BlueHalo, United States; Subash Khanal, Nathan Jacobs, Washington University in St. Louis, United States
TU2.R13.4: LABEL NOISE ROBUST IMAGE REPRESENTATION LEARNING BASED ON SUPERVISED VARIATIONAL AUTOENCODERS IN REMOTE SENSING
Gencer Sumbul, Begüm Demir, Technische Universität Berlin, Germany
TU2.R13.5: SEMI-SUPERVISED DEEP LEARNING REPRESENTATIONS IN EARTH OBSERVATION BASED FOREST MANAGEMENT
Oleg Antropov, Matthieu Molinier, VTT Technical Research Centre of Finland, Finland; Ridvan Salih Kuzu, German Aerospace Centre, Germany; Lloyd Hughes, Marc Rußwurm, Devis Tuia, Ecole Polytechnique Federale de Lausanne, Switzerland; Octavian Corneliu Dumitru, German Aerospace Centre, Germany; Shaojia Ge, Nanjing University of Science and Technology, China; Sudipan Saha, Indian Institute of Technology Delhi, India; Xiaoxiang Zhu, Technical University of Munich, Germany
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