Recent advances in remote sensors with higher spectral, spatial, and temporal resolutions have significantly increased data volumes, which pose a challenge to process and analyze the resulting massive data in a timely fashion to support practical applications. Meanwhile, the development of computationally demanding Machine Learning (ML) and Deep Learning (DL) techniques (e.g., deep neural networks with massive amounts of tunable parameters) demand for parallel algorithms with high scalability performance. Therefore, data intensive computing approaches have become indispensable tools to deal with the challenges posed by applications from geoscience and remote sensing. In recent years, high-performance and distributed computing have been rapidly advanced in terms of hardware architectures and software. For instance, the popular graphics processing unit(GPU) has evolved into a highly parallel many-core processor with tremendous computing power and high memory bandwidth. Moreover, recent High Performance Computing (HPC) architectures and parallel programming have been influenced by the rapid advancement of DL and hardware accelerators as modern GPUs.
This special session aims at gathering a collection of papers in the most advanced and trendy areas interested in exploiting new high-performance and distributed computing technologies and algorithms to expedite the processing and analysis of big remote sensing data.
- 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
- 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
- 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
- ENHANCING LARGE BATCH SIZE TRAINING OF DEEP MODELS FOR REMOTE SENSING APPLICATIONS Rocco Sedona, Gabriele Cavallaro, Forschungszentrums Jülich, Germany; Morris Riedel, Matthias Book, University of Iceland, Iceland
- 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
Gabriele Cavallaro (Forschungszentrum Jülich)
Dora Blanco Heras (University of Santiago de Compostela)
This special session collects papers in the most advanced and trendy areas interested in exploiting new high-performance and distributed computing technologies and algorithms to expedite the processing and analysis of big remote sensing data.To the event page
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