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

TH4.O-4: Data Intensive Computing for Remote Sensing

Thu, 15 Jul, 14:40 - 16:10 (UTC)
Thu, 15 Jul, 16:40 - 18:10 Central Europe Summer Time (UTC +2)
Thu, 15 Jul, 22:40 - 00:10 China Standard Time (UTC +8)
Thu, 15 Jul, 10:40 - 12:10 Eastern Daylight Time (UTC -4)

Session Co-Chairs: Gabriele Cavallaro, Forschungszentrum Jülich and Dora Heras, University of Santiago de Compostela

Session Manager: Marcel Stefko

Track: Invited Sessions

TH4.O-4.1: PRACTICE AND EXPERIENCE IN USING PARALLEL AND SCALABLE MACHINE LEARNING IN REMOTE SENSING FROM HPC OVER CLOUD TO QUANTUM COMPUTING

Morris Riedel, University of Iceland, Iceland; Gabriele Cavallaro, Forschungszentrum Jülich, Germany; Jón Atli Benediktsson, University of Iceland, Iceland

TH4.O-4.3: Comparing area-based and feature-based methods for co-registration of multispectral bands on GPU

Álvaro Ordóñez, Dora B. Heras, Francisco Argüello, Universidade de Santiago de Compostela, Spain

TH4.O-4.4: AN FPGA-BASED IMPLEMENTATION OF A HYPERSPECTRAL ANOMALY DETECTION ALGORITHM FOR REAL-TIME APPLICATIONS

Maria Diaz, University of Las Palmas de Gran Canaria (ULPGC), Spain; Julian Caba, University of Castilla La Mancha (UCLM), Spain; Raul Guerra, University of Las Palmas de Gran Canaria (ULPGC), Spain; Jesus Barba, University of Castilla La Mancha (UCLM), Spain; Sebastian Lopez, University of Las Palmas de Gran Canaria (ULPGC), Spain

TH4.O-4.5: ENHANCING LARGE BATCH SIZE TRAINING OF DEEP MODELS FOR REMOTE SENSING APPLICATIONS

Rocco Sedona, Gabriele Cavallaro, Forschungszentrum Jülich, Germany; Morris Riedel, Matthias Book, University of Iceland, Iceland

TH4.O-4.6: EVOLUTIONARY OPTIMIZATION OF NEURAL ARCHITECTURES IN REMOTE SENSING CLASSIFICATION PROBLEMS

Daniel Coquelin, Karlsruher Institut für Technologie, Germany; Rocco Sedona, Morris Riedel, Forschungszentrum Jülich / University of Iceland, Germany; Markus Götz, Karlsruher Institut für Technologie, Germany

TH4.O-4.1: PRACTICE AND EXPERIENCE IN USING PARALLEL AND SCALABLE MACHINE LEARNING IN REMOTE SENSING FROM HPC OVER CLOUD TO QUANTUM COMPUTING

TH4.O-4.3: Comparing area-based and feature-based methods for co-registration of multispectral bands on GPU

TH4.O-4.4: AN FPGA-BASED IMPLEMENTATION OF A HYPERSPECTRAL ANOMALY DETECTION ALGORITHM FOR REAL-TIME APPLICATIONS

TH4.O-4.5: ENHANCING LARGE BATCH SIZE TRAINING OF DEEP MODELS FOR REMOTE SENSING APPLICATIONS

TH4.O-4.6: EVOLUTIONARY OPTIMIZATION OF NEURAL ARCHITECTURES IN REMOTE SENSING CLASSIFICATION PROBLEMS

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