Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Holistic Model for HTTP Botnet Detection Based on DNS Traffic Analysis Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Spectral-Spatial Classification of Hyperspectral Imagery Using Support Vector and Fuzzy-MRF

Authors : Sumit Chakravarty, Madhushri Banerjee, Sonali Chandel

Published in: Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Hyper-Spectral Image (HSI) classification is one of the essential problems in hyperspectral image processing. It has been researched extensively and has resulted in a variety of publications. A key approach investigated in recent years incorporates both spectral and spatial characteristics to analyze the hyperspectral data. In this paper we have presented our proposed approach to improve the accuracy of HSI classification. Support Vector Machines have been used to classify spectral characteristics of images in conjunction with Markov Random Fields that classify HSI using spatial means. However, this current technique of combining them does not enforce smoothness in spatial and spectral analyses. We ensure finer segmentations in the results by adding our innovative approach of including Fuzzy-Markov Random Field to spectral classification. The ‘fuzziness’ promotes smoother transitions among classified pixels while preserving region integrity. Results show the efficacy of our approach.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Security Protocol of Social Payment Apps
next chapter Infant Monitoring System Using Wearable Sensors Based on Blood Oxygen Saturation: A Review
Literature
1.
Arai, M., Okumura, K., Satake, M., Shimizu, T.: Proteome-wide functional classification and identification of prokaryotic transmembrane proteins by transmembrane topology similarity comparison. Protein Sci. 13, 2170–2183 (2004)CrossRef
2.
He, M., Imran, F.M., Belkacem B., Mei, S.: Improving hyperspectral image classification accuracy using Iterative SVM with spatial-spectral information. In: 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, pp. 471–475 (2013). doi:10.​1109/​ChinaSIP.​2013.​6625384
3.
Khodadadzadeh, M., Rajabi, R., Ghassemian, H.: A novel approach for spectral-spatial classification of hyperspectral data based on SVM-MRF method. In: 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, pp. 1890–1893 (2011). doi:10.​1109/​IGARSS.​2011.​6049493
4.
Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian H., Bioucas-Dias, J.M.: Spectral-spatial classification for hyperspectral data using SVM and subspace MLR. In: 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, Melbourne, VIC, pp. 2180–2183 (2013). doi:10.​1109/​IGARSS.​2013.​6723247
5.
Borhani, M., Ghassemian, H.: Hyperspectral image classification based on spectral-spatial features using probabilistic SVM and locally weighted Markov random fields. In: 2014 Iranian Conference on Intelligent Systems (ICIS), Bam, pp. 1–6 (2014). doi:10.​1109/​IranianCIS.​2014.​6802573
6.
Quesada-Barriuso, P., Argüello, F., Heras, D.B.: Spectral–spatial classification of hyperspectral images using wavelets and extended morphological profiles. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(4), 1177–1185 (2014). doi:10.​1109/​JSTARS.​2014 CrossRef
7.
8.
Ma, A., Zhong, Y., Zhao, B., Jiao, H., Zhang, L.: Spectral-spatial DNA encoding discriminative classifier for hyperspectral remote sensing imagery. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, pp. 1710–1713 (2015). doi:10.​1109/​IGARSS.​2015.​7326117
9.
He, Z., Shen, Y., Zhang, M., Wang, Q., Wang, Y., Yu, R.: Spectral-spatial hyperspectral image classification via SVM and superpixel segmentation. In: 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, Montevideo, pp. 422–427 (2014). doi:10.​1109/​I2MTC.​2014.​6860780
10.
Poorahangaryan, F., Ghassemian, H.: Spectral-spatial hyperspectral classification with spatial filtering and minimum spanning forest. In: 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP), Tehran, pp. 49–52 (2015). doi:10.​1109/​IranianMVIP.​2015.​7397502
11.
Wang, J., Jiao, L., Wang, S., Hou, B., Liu, F.: Adaptive Nonlocal Spatial-Spectral Kernel for Hyperspectral Imagery Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(9), 4086–4101 (2016). doi:10.​1109/​JSTARS.​2016.​2526604 CrossRef
12.
Yu, H., Gao, L., Li, J., Zhang, B.: Spectral-spatial classification based on subspace support vector machine and Markov random field. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, pp. 2783–2786 (2016). doi:10.​1109/​IGARSS
13.
Izhar, et. al.: Fuzzy logic based edge detection for noisy images. Tech. J. 20(SI)(II(S)) (2015). University of Engineering and Technology (UET) Taxila, Pakistan
14.
Baumgardner, M.F., Biehl, L.L., Landgrebe, D.A.: 220 band AVIRIS hyperspectral image data set: june 12, 1992 indian pine test site 3. Purdue University Research Repository (2015). doi:10.​4231/​R7RX991C
15.
16.
Vapnik, V., Chervonenkis, A.: The necessary and sufficient conditions forconsistencyin the empirical risk minimization method. Pattern Recogn. Image Anal. 1(3), 283–305 (1991)
17.
Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004)CrossRef
18.
Wu, T.-F., Lin, C.-J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)MathSciNetMATH
19.
20.
Chang, C.-I.: Hyperspectral Data Exploitation: Theory and Applications. Wiley, New York (2007)CrossRef
21.
Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis, 4th edn. Springer, Heidelberg (2006)
Metadata
Title
Spectral-Spatial Classification of Hyperspectral Imagery Using Support Vector and Fuzzy-MRF
Authors
Sumit Chakravarty
Madhushri Banerjee
Sonali Chandel
Copyright Year
2017
Book
Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments

Print ISBN: 978-3-319-69154-1

Electronic ISBN: 978-3-319-69155-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69155-8_11

Premium Partner

    Image Credits