IEEE IGARSS 2021 || Brussels, Belgium || July 12–14, 2021 • Physical

TU3.O-7.2

Data Augmentation for Land Cover Classification using Generative Adversarial Networks

Kamel Aouaidjia, Issam Boukerch, Algerian Space Agency, Algeria

Session:

Image Segmentation: Data and Applications

Track:

Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)

Presentation Time:

Tue, 13 Jul, 12:40-12:55 (UTC)
Tue, 13 Jul, 14:40-14:55 Central Europe Summer Time (UTC +2)
Tue, 13 Jul, 20:40-20:55 China Standard Time (UTC +8)
Tue, 13 Jul, 08:40-08:55 Eastern Daylight Time (UTC -4)

Session Co-Chairs:

Alina Zare, University of Florida and Kruspe Anna, German Aerospace Center (DLR); Technical University of Munich (TUM)

Session Manager:

Xingyan Cao

Session TU3.O-7

TU3.O-7.1: EXPLORING TEMPORAL CONTEXT AT MULTIPLE SCALES FOR CROP MAPPING WITH FULLY CONVOLUTIONAL RECURRENT NETS AND FULLY CONNECTED CRFS

Marcos Rogozinski, Jorge Andres Chamorro Martinez, Patrick Nigri Happ, Raul Queiroz Feitosa, Pontifical Catholic University of Rio de Janeiro, Brazil

TU3.O-7.2: Data Augmentation for Land Cover Classification using Generative Adversarial Networks

Kamel Aouaidjia, Issam Boukerch, Algerian Space Agency, Algeria

TU3.O-7.3: THE WEAKLY-LABELED RAND INDEX

Dylan Stewart, Anna Hampton, Alina Zare, University of Florida, United States; Jeff Dale, James Keller, University of Missouri, United States

TU3.O-7.4: MODERATE RESOLUTION REMOTE SENSING AND MACHINE LEARNING FOR HUMAN RIGHTS MONITORING: THE CASE OF RAKHINE STATE, MYANMAR

Josh Redmond, University of Exeter, United Kingdom

TU3.O-7.5: An OpenStreetMap-Based Dataset of Building Footprints For Analysing Different Types of Label Noise

Jonas Gütter, German Aerospace Center (DLR), Jena, Germany, Germany; Kruspe Anna, Xiao Xiang Zhu, German Aerospace Center (DLR); Technical University of Munich (TUM), Germany

TU3.O-7.6: ATTENTION BASED SEMANTIC SEGMENTATION ON UAV DATASET FOR NATURAL DISASTER DAMAGE ASSESSMENT

Tashnim Chowdhury, Maryam Rahnemoonfar, University of Maryland Baltimore County, United States

Data Augmentation for Land Cover Classification using Generative Adversarial Networks

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