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Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns

  • Cai-ling Wang
  • Jinchang Ren
  • Hong-wei Wang
  • Yinyong Zhang
  • Jia Wen
Article
  • 43 Downloads

Abstract

It is of great interest in spectral-spatial features classification for hyperspectral images (HSI) with high spatial resolution. This paper presents a novel Spectral-spatial classification method for improving hyperspectral image classification accuracy. Specifically, a new texture feature extraction algorithm exploits spatial texture feature from spectrum is proposed. It employs local binary patterns (LBPs) in order to extract the image texture feature with respect to spectrum information diversity (SID) to measure the differences of spectrum information. The classifier adopted in this work is support vector machine (SVM) because of its outstanding classification performances. In this paper, two real hyperspectral image datasets are used for testing the performance of the proposed method. Our experimental results from real hyperspectral images indicate that the proposed framework can enhance the classification accuracy compare to traditional alternatives.

Keywords

Hyperspectral image classification Spectral-spatial analysis Local binary patterns Spectrum information diversity Support vector machine 

Notes

Acknowledgments

This work was supported in part by National Natural Science foundations of China (Grant Nos. 41301382, 61401439, 41604113, 41711530128) and foundation of Key lab of spectral imaging, Xi’an Institute of Optics and Precision Mechanics of CAS.

References

  1. 1.
    Bandos TV, Bruzzone L, Camps-Valls G (2009) Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans Geosc Remote Sens 47(3):862–873CrossRefGoogle Scholar
  2. 2.
    Benediktsson JA, Palmason JA, Sveinsson JR (2005) Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosc Remote Sen 43(3):480–491CrossRefGoogle Scholar
  3. 3.
    Camps-Valls G et al (2006) Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sens Lett 3(1):93–97CrossRefGoogle Scholar
  4. 4.
    Chang CI (2000) An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis. IEEE Trans Inf Theory 46(5):1927–1932CrossRefzbMATHGoogle Scholar
  5. 5.
    Chen C et al (2014) Spectral–spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification. IEEE J Sel Topics Appl Earth Observ Remote Sens 7(4):1047–1059CrossRefGoogle Scholar
  6. 6.
    Di Zenzo S et al (1987) Gaussian maximum likelihood and contextual classification algorithms for multicrop classification. IEEE Trans Geosci Remote Sen GE-25(6):815–824CrossRefGoogle Scholar
  7. 7.
    Doshi NP, Schaefer G, Zhu SY (2015) An evaluation of lbp texture descriptors for the classification of HEp-2 cells. IEEE International Conference on Systems, Man, and Cybernetics IEEEGoogle Scholar
  8. 8.
    Fang L, Li S, Duan W, Ren J, Benediktsson JA (2015) Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels. IEEE Trans Geosci Remote Sens 53(12):6663–6674 Google Scholar
  9. 9.
    Gong Y, Lazebnik S (2011) Iterative quantization: a procrustean approach to learning binary codes. IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp 817–824Google Scholar
  10. 10.
    Gu Y et al (2012) Representative multiple Kernel learning for classification in hyperspectral imagery. IEEE Trans Geosc Remote Sen 50(7):2852–2865CrossRefGoogle Scholar
  11. 11.
    Guo Y et al (2017) Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Trans Image Process PP(99):1–1MathSciNetCrossRefGoogle Scholar
  12. 12.
    Guo Y et al (2017) Zero-shot learning with transferred samples. IEEE Trans Image Process 26(7):3277–3290MathSciNetCrossRefGoogle Scholar
  13. 13.
    Huang X, Zhang L (2013) An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Trans Geosc Remote Sen 51(1):257–272CrossRefGoogle Scholar
  14. 14.
    Kumar V, Vijaya KSR, Krishna VV (2015) Face recognition using prominent LBP model. Int J Appl Eng Res 10(2):4373–4384Google Scholar
  15. 15.
    Li W et al (2012) Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Trans Geosc Remote Sens 50(4):1185–1198CrossRefGoogle Scholar
  16. 16.
    Li J et al (2013) Generalized composite Kernel framework for hyperspectral image classification. IEEE Trans Geosc Remote Sen 51(9):4816–4829CrossRefGoogle Scholar
  17. 17.
    Li W et al (2015) Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosc Remote Sen 53(7):1–13CrossRefGoogle Scholar
  18. 18.
    Lin Z et al (2016) Cross-view retrieval via probability-based semantics-preserving hashing. IEEE Trans Cybern PP(99):1–14Google Scholar
  19. 19.
    Ma L, Crawford MM, Tian J (2010) Local manifold learning-based, -nearest-neighbor for hyperspectral image classification. IEEE Trans Geosci Remote Sens 48(11):4099–4109Google Scholar
  20. 20.
    Masood K, Rajpoot N (1945) Texture based classification of hyperspectral colon biopsy samples using CLBP. Proceedings, pp 1011–1014Google Scholar
  21. 21.
    Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosc Remote Sen 42(8):1778–1790CrossRefGoogle Scholar
  22. 22.
    Mura MD et al (2010) Extended profiles with morphological attribute filters for the analysis of hyperspectral data. Int J Remote Sens 31(22):5975–5991CrossRefGoogle Scholar
  23. 23.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefzbMATHGoogle Scholar
  24. 24.
    Richards JA (1986) Remote sensing digital image analysis. Remote sensing digital image analysis. Springer-Verlag, Berlin, pp 47–54CrossRefGoogle Scholar
  25. 25.
    Stathakis D, Vasilakos A (2006) Comparison of computational intelligence based classification techniques for remotely sensed optical image classification. IEEE Trans Geosci Remote Sens 44(8):2305–2318CrossRefGoogle Scholar
  26. 26.
    Tuia D et al (2010) Learning relevant image features with multiple-Kernel classification. IEEE Trans Geosc Remote Sen 48(10):3780–3791CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceXi’an Shiyou UniversityXi’anChina
  2. 2.Department of Electrical and Electronic EngineeringUniversity of StrathclydeGlasgowUK
  3. 3.Engineering University of CAPFXi’anChina
  4. 4.School of electronics EngineeringTianjin Polytechnic UniversityTianjinChina

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