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Dimensionality Reduction Using Band Correlation and Variance Measure from Discrete Wavelet Transformed Hyperspectral Imagery

  • Arati PaulEmail author
  • Nabendu Chaki
Article
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Abstract

Contiguous narrow bands of hyperspectral images greatly increase computational complexity. Redundancy reduction is therefore necessary. Here, a minimum redundancy and maximum variance based unsupervised band selection methodology is proposed. Discrete wavelet transformation is applied on the data to reduce spatial redundancy without much effecting the overall band correlations. This in turn made the process more time efficient and noise resilient. Highly correlated bands are considered similar, and one with higher variance is accepted as being more discriminating. Finally, classification is performed with the selected bands and overall accuracy (OA) is calculated. The proposed method is compared with four other existing state-of-the-art methods in the similar field in terms of OA and execution time for evaluating the performance.

Keywords

Band elimination Correlation DWT Hyperspectral Unsupervised 

Notes

References

  1. 1.
    An D et al (2016) Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta. Int J Remote Sens 37(2):455–470CrossRefGoogle Scholar
  2. 2.
    Deepa P, Thilagavathi K (2015) Feature extraction of hyperspectral image using principal component analysis and folded-principal component analysis. In: 2nd International conference on electronics and communication systems (ICECS), IEEE, pp 656–660Google Scholar
  3. 3.
    Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach, 1st edn. Prentice-Hall International, New DelhiGoogle Scholar
  4. 4.
    Ding L, Tang P, Li H (2013) Isomap-based subspace analysis for the classification of hyperspectral data. In: IEEE international geoscience and remote sensing symposium (IGARSS). IEEE, pp 429–432Google Scholar
  5. 5.
    Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, New YorkGoogle Scholar
  6. 6.
    Feng J, Jiao L, Liu F, Sun T, Zhang X (2016) Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recogn 51:295–309CrossRefGoogle Scholar
  7. 7.
    Feng J, Jiao L, Sun T, Liu H, Zhang X (2016) Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection. IEEE Trans Geosci Remote Sens 54(11):6516–6530CrossRefGoogle Scholar
  8. 8.
    Fukunaga K (2013) Introduction to statistical pattern recognition. Elsevier, AmsterdamGoogle Scholar
  9. 9.
    Ghorbanian A, Mohammadzadeh A (2018) An unsupervised feature extraction method based on band correlation clustering for hyperspectral image classification using limited training samples. Remote Sens Lett 9(10):982–991.  https://doi.org/10.1080/2150704X.2018.1500723 CrossRefGoogle Scholar
  10. 10.
    Gonzalez RC, Woods R (2007) Digital image processing. Pearson Prentice Hall, Upper Saddle RiverGoogle Scholar
  11. 11.
    Götze C, Beyer F, Gläßer C (2016) Pioneer vegetation as an indicator of the geochemical parameters in abandoned mine sites using hyperspectral airborne data. Environ Earth Sci 75:613CrossRefGoogle Scholar
  12. 12.
    Hughes G (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 14(1):55–63.  https://doi.org/10.1109/TIT.1968.1054102 CrossRefGoogle Scholar
  13. 13.
    Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37CrossRefGoogle Scholar
  14. 14.
    Jia S, Tang G, Zhu J, Li Q (2016) A novel ranking-based clustering approach for hyperspectral band selection. IEEE Trans Geosci Remote Sens 54(1):88–102CrossRefGoogle Scholar
  15. 15.
    Kim B, Landgrebe DA (1991) Hierarchical classifier design in highdimensional numerous class cases. IEEE Trans Geosci Remote Sens 29(4):518–528CrossRefGoogle Scholar
  16. 16.
    Launeau P et al (2017) Airborne hyperspectral mapping of trees in an urban area. Int J Remote Sens 38(5):1277–1311CrossRefGoogle Scholar
  17. 17.
    Li W, Prasad S, Fowler JE, Bruce LM (2012) Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Trans Geosci Remote Sens 50(4):1185–1198CrossRefGoogle Scholar
  18. 18.
    Paul A, Bhattacharya S, Dutta D, Sharma JR, Dadhwal VK (2015) Band selection in hyperspectral imagery using spatial cluster mean and genetic algorithms. GISci Remote Sens 52(6):644–661.  https://doi.org/10.1080/15481603.2015.1075180 CrossRefGoogle Scholar
  19. 19.
    Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8:1027–1061Google Scholar
  20. 20.
    Sun K, Geng X, Ji L (2015) Exemplar component analysis: a fast band selection method for hyperspectral imagery. IEEE Geosci Remote Sens Lett 12(5):998–1002CrossRefGoogle Scholar
  21. 21.
    Sun K, Geng X, Ji L, Lu X (2014) A new band selection method for hyperspectral image based on data quality. IEEE J Sel Top Appl Earth Observ Remote Sens 7(6):2697–2703CrossRefGoogle Scholar
  22. 22.
    Sun Y, Wang S, Liu Q, Hang R, Liu G (2017) Hypergraph embedding for spatial-spectral joint feature extraction in hyperspectral images. Remote Sens 9(5):506.  https://doi.org/10.3390/rs9050506 CrossRefGoogle Scholar
  23. 23.
    Upadhyay V, Kumar A (2018) Hyperspectral remote sensing of forests: technological advancements, opportunities and challenges. Earth Sci Inf 11:487.  https://doi.org/10.1007/s12145-018-0345-7 CrossRefGoogle Scholar
  24. 24.
    Wang C, Gong M, Zhang M, Chan Y (2015) Unsupervised hyperspectral image band selection via column subset selection. IEEE Geosci Remote Sens Lett 12(7):1411–1415CrossRefGoogle Scholar
  25. 25.
    Wang Q, Lin J, Yuan Y (2016) Salient band selection for hyperspectral image classification via manifold ranking. IEEE Trans Neural Netw Learn Syst 27(6):1279–1289CrossRefGoogle Scholar
  26. 26.
    Yang H, Du Q, Su H, Sheng Y (2011) An efficient method for supervised hyperspectral band selection. IEEE Geosci Remote Sens Lett 8(1):138–142CrossRefGoogle Scholar
  27. 27.
    Yuan H, Tang YY (2015) Learning with hypergraph for hyperspectral image feature extraction. Geosci Remote Sens Lett IEEE 12(8):1695–1699CrossRefGoogle Scholar
  28. 28.
    Yuan Y, Lin J, Wang Q (2016) Dual-clustering-based hyperspectral band selection by contextual analysis. IEEE Trans Geosci Remote Sens 54(3):1431–1445CrossRefGoogle Scholar
  29. 29.
    Zarco-Tejada P, González-Dugo M, Fereres E (2016) Seasonal stability of chlorophyll fluorescence quantified from airborne hyperspectral imagery as an indicator of net photosynthesis in the context of precision agriculture. Remote Sens Environ 179:89–103CrossRefGoogle Scholar
  30. 30.
    Zhang W, Li X, Zhao L (2018) A fast hyperspectral feature selection method based on band correlation analysis. IEEE Geosci Remote Sens Lett 15(11):1750–1754CrossRefGoogle Scholar
  31. 31.
    Zhao W, Du S (2016) Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544–4554.  https://doi.org/10.1109/TGRS.2016.2543748 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Regional Remote Sensing Centre-EastISROKolkataIndia
  2. 2.Department of Computer Science and EngineeringUniversity of CalcuttaKolkataIndia

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