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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

FR4.O-13.5

TRUSTING SMALL TRAINING DATASET FOR SUPERVISED CHANGE DETECTION

Sudipan Saha, Technical University of Munich, Germany; Biplab Banerjee, Indian Institute of Technology Bombay, India; Xiao Xiang Zhu, German Aerospace Center (DLR), Germany
Session:
Machine Learning with Small Earth Observation Datasets
Track:
Invited Sessions
Presentation Time:
Fri, 16 Jul, 15:40-15:55 (UTC)
Fri, 16 Jul, 17:40-17:55 Central Europe Summer Time (UTC +2)
Fri, 16 Jul, 23:40-23:55 China Standard Time (UTC +8)
Fri, 16 Jul, 11:40-11:55 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Matthieu Molinier, VTT Technical Research Centre of Finland Ltd and Hanna Meyer, University of Münster
Session Manager:
Songyao Huai
Presentation
Discussion
Resources
Session FR4.O-13
FR4.O-13.1: The trap of random sampling and how to avoid it - Alternative sampling strategies for a realistic estimate of the generalization error in remote sensing
Ronny Hänsch, German Aerospace Center (DLR), Germany
FR4.O-13.3: INVESTIGATING THE IMPACT OF THE TRAINING SET SIZE ON DEEP LEARNING-POWERED HYPERSPECTRAL UNMIXING
Lukasz Tulczyjew, KP Labs, Poland; Jakub Nalepa, KP Labs / Silesian University of Technology, Poland
FR4.O-13.4: ESTIMATING THE AREA OF APPLICABILITY OF REMOTE SENSING-BASED MACHINE LEARNING MODELS WITH LIMITED TRAINING DATA
Hanna Meyer, Edzer Pebesma, University of Münster, Germany
FR4.O-13.5: TRUSTING SMALL TRAINING DATASET FOR SUPERVISED CHANGE DETECTION
Sudipan Saha, Technical University of Munich, Germany; Biplab Banerjee, Indian Institute of Technology Bombay, India; Xiao Xiang Zhu, German Aerospace Center (DLR), Germany
FR4.O-13.6: PATCH SIZE SELECTION FOR ANALYSIS OF SUB-METER RESOLUTION HYPERSPECTRAL IMAGERY OF FORESTS
Matti Mõttus, Matthieu Molinier, Eelis Halme, VTT Technical Research Centre of Finland, Finland; Hai Cu, Jorma Laaksonen, Aalto University, Finland

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