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@INBOOK{Sedona:1029396,
author = {Sedona, Rocco and Cavallaro, Gabriele and Riedel, Morris
and Benediktsson, Jón Atli},
title = {{P}roven {A}pproaches of {U}sing {I}nnovative
{H}igh-{P}erformance {C}omputing {A}rchitectures in {R}emote
{S}ensing},
address = {Boca Raton},
publisher = {CRC Press},
reportid = {FZJ-2024-05104},
isbn = {9781003382010},
pages = {432},
year = {2024},
comment = {Signal and Image Processing for Remote Sensing},
booktitle = {Signal and Image Processing for Remote
Sensing},
abstract = {This chapter underscores the essential role of
high-performance computing (HPC) in the realm of remote
sensing (RS), effectively addressing the growing demand for
processing extensive and complex datasets. HPC, empowered by
parallel programming paradigms, significantly speeds up a
range of tasks, including image processing, data mining, and
modeling, vital in the context of Earth observation (EO)
applications. More notably, HPC can build even better models
by employing systematic hyperparameter optimization methods
that are computationally demanding, given a large search
space. Furthermore, with deep learning (DL) progressively
gravitating toward foundation models, extensively trained on
substantial datasets, endowing them with the remarkable
capability to transfer knowledge across diverse tasks, there
is an increased demand for computational resources in the
fast-paced landscape of artificial intelligence (AI) and
consequently a heightened interest in HPC. Solutions to
provide optimized resources on HPC resources, however, have
increased their complexity and heterogeneity. This chapter
highlights the advantages of embracing HPC while
acknowledging current challenges, solutions, and future
trends.},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511)},
pid = {G:(DE-HGF)POF4-5111},
typ = {PUB:(DE-HGF)7},
url = {https://juser.fz-juelich.de/record/1029396},
}