nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Parallel and Distributed Local Fisher Discriminant Analysis to Reduce Hyperspectral Images on Cloud Computing Architectures

verfasst von : Rania Zaatour, Sonia Bouzidi, Ezzeddine Zagrouba

Erschienen in: Advanced Concepts for Intelligent Vision Systems

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Hyperspectral images are data cubes that offer very rich spectral and spatial resolutions. These images are so highly dimensioned that we generally reduce them in a pre-processing step in order to process them efficiently. In this context, Local Fisher Discriminant Analysis (LFDA) is a feature extraction technique that proved better than several commonly used dimensionality reduction techniques. However, this method suffers from memory problems and long execution times on commodity hardware. In this paper, to solve these problems, we first added an optimization step to LFDA to make it executable on commodity hardware and to make it suitable for parallel and distributed computing, then, we implemented it in a parallel and distributed way using Apache Spark. We tested our implementation on Amazon Web Services (AWS)’s Elastic MapReduce (EMR) clusters, using different hyperspectral images with different sizes. This proved higher performances with a speedup of up to 70x.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Unsupervised Perception Model for UAVs Landing Target Detection and Recognition

Nächstes Kapitel Bayesian Vehicle Detection Using Optical Remote Sensing Images

Available online at www.ms.k.u-tokyo.ac.jp/software.html#LFDA.

Amazon Web Services (AWS) - Cloud Computing Services (aws.amazon.com).

Amazon S3 aws.amazon.com/s3.

Available online at www.github.com/boto/boto3.

Amazon EMR aws.amazon.com/emr.

Amazon EC2 Instance Types (aws.amazon.com/ec2/instance-types).

Apache Hadoop YARN - Yet Another Resource Negotiator hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YARN.html.

Wu, Z., Yonglong, L., Antonio, P., Li, J., Xiao, F., Wei, Z.: Parallel and distributed dimensionality reduction of hyperspectral data on cloud computing architectures. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 9(6), 2270–2278 (2016)CrossRef

Haut, J.M., Paoletti, M., Plaza, J., Plaza, A.: Cloud implementation of the k-means algorithm for hyperspectral image analysis. J. Supercomput. 73(1), 514–529 (2017)CrossRef

Quirita, V.A.A., et al.: A new cloud computing architecture for the classification of remote sensing data. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(2), 409–416 (2017)CrossRef

Yin, X., Wu, Z., Liao, W., Wei, Z., Tan, C.: Cloud implementation of hyperspectral image restoration with PCA and total variation based on spark. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3405–3408. IEEE (2017)

Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8, 1027–1061 (2007)MATH

Shen, P., Lu, X., Liu, L., Kawai, H.: Local fisher discriminant analysis for spoken language identification. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5825–5829. IEEE (2016)

Chen, H.-L., Liu, D.-Y., Yang, B., Liu, J., Wang, G.: A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert. Syst. Appl. 38(9), 11796–11803 (2011)CrossRef

Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3318–3325 (2013)

Zhang, S., Lei, B., Chen, A., Chen, C., Chen, Y.: Spoken emotion recognition using local fisher discriminant analysis. In: 2010 IEEE 10th International Conference on Signal Processing (ICSP), pp. 538–540. IEEE (2010)

10.

Rahulamathavan, Y., Phan, R.C.-W., Chambers, J.A., Parish, D.J.: Facial expression recognition in the encrypted domain based on local fisher discriminant analysis. IEEE Trans. Affect. Comput. 4(1), 83–92 (2013)CrossRef

11.

Guo, J., Chen, H., Li, Y.: Palmprint recognition based on local fisher discriminant analysis. JSW 9(2), 287–292 (2014)CrossRef

12.

Li, W., Prasad, S., Fowler, J.E., Bruce, L.M.: Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Trans. Geosci. Remote. Sens. 50(4), 1185–1198 (2012)CrossRef

13.

Li, W., Prasad, S., Fowler, J.E.: Hyperspectral image classification using gaussian mixture models and markov random fields. IEEE Geosci. Remote. Sens. Lett. 11(1), 153–157 (2014)CrossRef

14.

Zaatour, R., Bouzidi, S., Zagrouba, E.: Impact of feature extraction and feature selection techniques on extended attribute profile-based hyperspectral image classification. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol.4, VISAPP, (VISIGRAPP 2017), pp. 579–586. INSTICC, ScitePress (2017). ISBN 978-989-758-225-7, https://doi.org/10.5220/0006171305790586

15.

Zaharia, M.: Fast and interactive analytics over hadoop data with spark. Usenix Login 37(4), 45–51 (2012)MathSciNet

16.

Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)CrossRef

17.

Niyogi, X.: Locality preserving projections. In: Neural Information Processing Systems, vol. 16, p. 153. MIT (2004)

18.

Oliphant, T.E.: Scipy: open source scientific tools for python. Comput. Sci. Eng. 9, 10–20 (2007)CrossRef

19.

Oliphant, Travis E.: A Guide to NumPy, vol. 1. Trelgol Publishing USA (2006)

Titel: Parallel and Distributed Local Fisher Discriminant Analysis to Reduce Hyperspectral Images on Cloud Computing Architectures
verfasst von: Rania Zaatour
Sonia Bouzidi
Ezzeddine Zagrouba
Verlag: Springer International Publishing
Buch: Advanced Concepts for Intelligent Vision Systems
Print ISBN: 978-3-030-01448-3

Electronic ISBN: 978-3-030-01449-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01449-0_21

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"