ICTMI 2017 pp 185-197 | Cite as

Feature Extraction-Based Hyperspectral Unmixing

  • M. R. Vimala Devi
  • S. KalaivaniEmail author
Conference paper


Purpose Hyperspectral imaging belongs to a class of techniques called spectral imaging or spectral analysis. Due to the high dimensionality of hyperspectral cubes, it is a very difficult task to select few informative bands from original hyperspectral remote sensing images. The dimensionality reduction of hyperspectral images is a pre-processing technique used to perform many applications like unmixing, classification, reconstruction and detection. Procedures Hyperspectral unmixing is an emerging topic in hyperspectral image analysis to distinguish the materials present in an image and thereby finding the proportion of each material in an image. The distinct materials are called as end members or spectral signatures, and proportion values are called as abundance fractions. This paper proposes a scale invariant feature transform (SIFT)-based dimension reduction with application to unmixing pixels in hyperspectral images. Results The proposed feature extraction-based selection of non-redundant informative bands followed by unmixing of pixels has been proved qualitatively and quantitatively with comparison to existing techniques principal component analysis, linear discriminant analysis-based unmixing. Another important advantage of the proposed method is that it takes into account the spectral variability in materials. Conclusion The proposed technique has been highlighted using the performance measures spectral angle distance and abundance angle distance.


Hyperspectral image Discriminant analysis Spectral signature Band selection Dimension reduction Spectral imaging Classification Detection Scale invariant feature transform Principal component analysis 


  1. 1.
    Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Signal Process Mag 19:44–57CrossRefGoogle Scholar
  2. 2.
    Bioucas-Dias JM, Plaza A, Dobigeon N, Parente M, Du Q, Gader P, Chanussot J (2012) Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J Sel Top Appl Earth Observations Remote Sens 5(2):354–379CrossRefGoogle Scholar
  3. 3.
    Shippert P (1980) Introduction to hyperspectral image analysis. Earth Sci Appl Res SystGoogle Scholar
  4. 4.
    Hapke BW (1981) Bidirectional reflectance spectroscopy. J Geophys Res 86:3039–3054CrossRefGoogle Scholar
  5. 5.
    Lodha Shraddha P, Kamalapur SM (2014) Dimension reduction for hyperspectral images. Int J of Application or Innovation in Engineering and Management  (IJAIEM) 3(10) Google Scholar
  6. 6.
    Ertürk A, Plaza A (2015) Informative change detection by unmixing for hyperspectral images. IEEE Geosci Remote Sens Lett 12(6):1252–1256CrossRefGoogle Scholar
  7. 7.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110MathSciNetCrossRefGoogle Scholar
  8. 8.
    Antonapolus P, Nikolaidis N, Pitas I (May 2007) Hierarchical face clustering using SIFT fetures. In: IEEE symposium on computational intelligence in image and signal processing, 2007. CIISP May 2007Google Scholar
  9. 9.
    Zare A, Ho KC (2014) Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing. IEEE Signal Process Mag 31:95–104CrossRefGoogle Scholar
  10. 10.
    Barberis A, Danese G, Leoporati F, Plaza A, Torti E (2013) Real-time implementation of the vertex component analysis algorithm on GPUs. IEEE Geosci Remote Sens Lett 10(2):251–255CrossRefGoogle Scholar
  11. 11.
    Chen J, Richard C, Honeine P (2012) Nonlinear unmixing of hyperspectral data based on a linear-mixture/nonlinear-fluctuation model. IEEE Trans Signal Process 1:480–492Google Scholar
  12. 12.
    Nascimento JMP, Bioucas-Dias JM (2005) Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans Geosci Remote Sens 43(4):898–910CrossRefGoogle Scholar
  13. 13.
    Keshava N (2003) A survey of spectral unmixing algorithms. Lincoln Lab J 14(1):55–78Google Scholar
  14. 14.
    Ferguson JP (1981) An introduction to hyperspectral imaging. Photonics and Analytic Marketing LtdGoogle Scholar
  15. 15.
    Vimala Devi MR, Kalaivani S (2016) A view on spectral unmixing in hyperspectral images. Far East J Electron Commun 23–32 (March 2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electronics EngineeringVIT UniversityVelloreIndia

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