Practice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing

Using computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth science. The utilization of efficient techniques through innovative computing systems in RS applications has become more widespread in recent...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE International Geoscience and Remote Sensing Symposium proceedings s. 1571 - 1574
Hlavní autoři: Riedel, Morris, Cavallaro, Gabriele, Benediktsson, Jon Atli
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 11.07.2021
Témata:
Cloud computing
deep learning
High performance computing
machine learning
parallel and distributed algorithms
Quantum computing
Real-time systems
remote sensing
Supercomputers
Training
Transfer learning
ISSN:2153-7003
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Using computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth science. The utilization of efficient techniques through innovative computing systems in RS applications has become more widespread in recent years. The continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') requires powerful computing resources with equally increasing performance. This paper reviews recent advances in High-Performance Computing (HPC), Cloud Computing (CC), and Quantum Computing (QC) applied to RS problems. It thus represents a snapshot of the state-of-the-art in ML in the context of the most recent developments in those computing areas, including our lessons learned over the last years. Our paper also includes some recent challenges and good experiences by using Europeans fastest supercomputer for hyper-spectral and multi-spectral image analysis with state-of-the-art data analysis tools. It offers a thoughtful perspective of the potential and emerging challenges of applying innovative computing paradigms to RS problems.
ISSN:2153-7003
DOI:10.1109/IGARSS47720.2021.9554656