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@ARTICLE{Haut:865543,
author = {Haut, Juan Mario and Gallardo, Jose Antonio and Paoletti,
Mercedes E. and Cavallaro, Gabriele and Plaza, Javier and
Plaza, Antonio and Riedel, Morris},
title = {{C}loud {D}eep {N}etworks for {H}yperspectral {I}mage
{A}nalysis},
journal = {IEEE transactions on geoscience and remote sensing},
volume = {57},
number = {12},
issn = {1558-0644},
address = {New York, NY},
publisher = {IEEE},
reportid = {FZJ-2019-04921},
pages = {9832 - 9848},
year = {2019},
abstract = {Advances in remote sensing hardware have led to a
significantly increased capability for high-quality data
acquisition, which allows the collection of remotely sensed
images with very high spatial, spectral, and radiometric
resolution. This trend calls for the development of new
techniques to enhance the way that such unprecedented
volumes of data are stored, processed, and analyzed. An
important approach to deal with massive volumes of
information is data compression, related to how data are
compressed before their storage or transmission. For
instance, hyperspectral images (HSIs) are characterized by
hundreds of spectral bands. In this sense, high-performance
computing (HPC) and high-throughput computing (HTC) offer
interesting alternatives. Particularly, distributed
solutions based on cloud computing can manage and store huge
amounts of data in fault-tolerant environments, by
interconnecting distributed computing nodes so that no
specialized hardware is needed. This strategy greatly
reduces the processing costs, making the processing of high
volumes of remotely sensed data a natural and even cheap
solution. In this paper, we present a new cloud-based
technique for spectral analysis and compression of HSIs.
Specifically, we develop a cloud implementation of a popular
deep neural network for non-linear data compression, known
as autoencoder (AE). Apache Spark serves as the backbone of
our cloud computing environment by connecting the available
processing nodes using a master-slave architecture. Our
newly developed approach has been tested using two widely
available HSI data sets. Experimental results indicate that
cloud computing architectures offer an adequate solution for
managing big remotely sensed data sets.},
cin = {JSC},
ddc = {620},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {512 - Data-Intensive Science and Federated Computing
(POF3-512)},
pid = {G:(DE-HGF)POF3-512},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000505701800026},
doi = {10.1109/TGRS.2019.2929731},
url = {https://juser.fz-juelich.de/record/865543},
}