Skip to main content
SpringerLink
Log in

Introducing spectral moment features in analyzing the SpecTex hyperspectral texture database

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Hyperspectral imaging provides more information than conventional RGB images. However, its high dimensionality prevents its adaptation to the existing image processing techniques. Defining full-band spectral feature is the first missing step, which is currently dealt with indirectly by band selection or dimension reduction. This article proposes a spectral feature extraction method using the mathematical moments to quantify the shape of the reflectance spectrum from different aspects. A whole family of features is presented by changing the moment attributes. All the features and their combinations are extensively tested in texture analysis of a new hyperspectral image database from textile samples (SpecTex). Two supervised experiments are performed: image patch classification and pixel-wise mosaic image segmentation. The proposed features are compared to four other features: the grayscale intensity, the RGB and CIELab values, and the principal components. Also, three analysis methods are tested: co-occurrence matrix, Gabor filter bank, and local binary pattern. In all cases, the moment features outperformed the opponents. Notably, combining the moment features with complementary attributes remarkably improved the performance. The most discriminative combinations are studied and formulated in this article.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Safia, A., He, D.-C.: Multiband compact texture unit descriptor for intra-band and inter-band texture analysis. ISPRS J. Photogramm. Remote Sens. 105, 169–185 (2015)

    Article  Google Scholar 

  2. Li, W., Chen, C., Su, H., Du, Q.: Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 53(7), 3681–3693 (2015)

    Article  Google Scholar 

  3. Brusco, N., Capeleto, S., Fedel, M., Paviotti, A., Poletto, L., Cortelazzo, G.M., Tondello, G.: A system for 3d modeling frescoed historical buildings with multispectral texture information. Mach. Vis. Appl. 17(6), 373–393 (2006)

    Article  Google Scholar 

  4. Pan, Z., Healey, G., Prasad, M., Tromberg, B.: Face recognition in hyperspectral images. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1552–1560 (2003)

    Article  Google Scholar 

  5. Bouatmane, S., Roula, M.A., Bouridane, A., Al-Maadeed, S.: Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery. Mach. Vis. Appl. 22(5), 865–878 (2011)

    Article  Google Scholar 

  6. Eckhard, T., Klammer, M., Valero, E.M., Hernández-Andrés, J.: Improved spectral density measurement from estimated reflectance data with kernel ridge regression. In: Image and Signal Processing, ser. Lecture Notes in Computer Science, pp. 79–86. Springer International Publishing (2014)

  7. Porebski, A., Vandenbroucke, N., Macaire, L.: Haralick feature extraction from LBP images for color texture classification. In: First Workshops on Image Processing Theory, Tools and Application, IPTA 2008, pp. 1–8 (2008)

  8. Khelifi, R., Adel, M., Bourennane, S.: Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images. EURASIP J. Adv. Signal Process. 2012(1), 118 (2012)

    Article  Google Scholar 

  9. Hauta-Kasari, M., Parkkinen, J., Jaaskelainen, T., Lenz, R.: Multi-spectral texture segmentation based on the spectral cooccurrence matrix. Pattern Anal. Appl. 2(4), 275–284 (1999)

    Article  Google Scholar 

  10. Münzenmayer, C., Volk, H., Küblbeck, C., Spinnler, K., Wittenberg, T.: Multispectral texture analysis using interplane sum-and difference-histograms. In: Joint Pattern Recognition Symposium. Springer, Berlin, Heidelberg, pp. 42–49 (2002)

  11. Ledoux, A., Losson, O., Macaire, L.: Color local binary patterns: compact descriptors for texture classification. J. Electron. Imaging 25(6), 061 404–061 404 (2016)

    Article  Google Scholar 

  12. Zhao, W., Du, S.: Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans. Geosci. Remote Sens. 54(8), 4544–4554 (2016)

    Article  Google Scholar 

  13. Xu, L., Wong, A., Li, F., Clausi, D.A.: Intrinsic representation of hyperspectral imagery for unsupervised feature extraction. IEEE Trans. Geosci. Remote Sens. 54(2), 1118–1130 (2016)

    Article  Google Scholar 

  14. Puetz, A.M., Olsen, R.C.: Haralick texture features expanded into the spectral domain. In: Defense and Security Symposium. International Society for Optics and Photonics, 623311 (2006)

  15. Nouri, D., Lucas, Y., Treuillet, S.: Hyperspectral interventional imaging for enhanced tissue visualization and discrimination combining band selection methods. Int. J. Comput. Assist. Radiol. Surg. 11(12), 2185–2197 (2016)

    Article  Google Scholar 

  16. Sharma, V., Van Gool, L.: Image-level classification in hyperspectral images using feature descriptors, with application to face recognition. arXiv:1605.03428 (2016)

  17. Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley, New York (2009)

    Book  MATH  Google Scholar 

  18. Zhang, L., Zhang, L., Tao, D., Huang, X.: On combining multiple features for hyperspectral remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 50(3), 879–893 (2012)

    Article  Google Scholar 

  19. Sinha, A., Banerji, S., Liu, C.: New color GPHOG descriptors for object and scene image classification. Mach. Vis. Appl. 25(2), 361–375 (2014)

    Article  Google Scholar 

  20. Mirmehdi, M., Xie, X., Suri, J.: Handbook of Texture Analysis. Imperial College Press, London (2008)

    Book  Google Scholar 

  21. Petrou, M., Sevilla, P.: Image Processing: Dealing with Texture. Wiley, New York (2006)

    Book  Google Scholar 

  22. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC–3(6), 610–621 (1973)

    Article  Google Scholar 

  23. Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognit. 24(12), 1167–1186 (1991)

    Article  Google Scholar 

  24. Mäenpää, T.: The Local Binary Pattern Approach to Texture Analysis: Extensions and Applications. Oulun yliopisto, Oulu (2003)

    Google Scholar 

  25. Kumar, B., Dikshit, O.: Spectral-spatial classification of hyperspectral imagery based on moment invariants. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 8(6), 2457–2463 (2015)

    Article  Google Scholar 

  26. Mirzapour, F., Ghassemian, H.: Moment-based feature extraction from high spatial resolution hyperspectral images. Int. J. Remote Sens. 37(6), 1349–1361 (2016)

    Article  Google Scholar 

  27. Zhang, Y., Wirkert, S.J., Iszatt, J., Kenngott, H., Wagner, M., Mayer, B., Stock, C., Clancy, N.T., Elson, D.S., Maier-Hein, L.: Tissue classification for laparoscopic image understanding based on multispectral texture analysis. In: SPIE Medical Imaging. International Society for Optics and Photonics, p. 978619 (2016)

  28. Kohonen, O.: Retrieval of Databased Spectral Images. Joensuu yliopistopaino, Joensuu (2007)

    Google Scholar 

  29. SpecTex database. [Online]. https://www.uef.fi/web/spectral/spectex

  30. Barra, V.: Expanding the local binary pattern to multispectral images using total orderings. In: International Conference on Computer Vision, Imaging and Computer Graphics. Springer, pp. 67–80 (2010)

  31. Song, C., Li, P., Yang, F.: Multivariate texture measured by Local binary pattern for multispectral image classification. IEEE Int. Conf. Geosci. Remote Sens. Symp. IGARSS 2006, 2145–2148 (2006)

    Google Scholar 

  32. Khelifi, R., Adel, M., Bourennane, S.: Segmentation of multispectral images based on band selection by including texture and mutual information. Biomed. Signal Process. Control 20, 16–23 (2015)

    Article  Google Scholar 

  33. Ledoux, A., Richard, N., Capelle-Laizé, A.S., Deborah, H., Fernandez-Maloigne, C.: Toward a full-band texture features for spectral images. IEEE Int. Conf. Image Process. (ICIP) 2014, 708–712 (2014)

    Google Scholar 

  34. Deborah, H., Richard, N., Hardeberg, J.Y.: On the quality evaluation of spectral image processing algorithms. In: Tenth International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), 2014, pp. 133–140 (2014)

  35. Deborah, H., Richard, N., Hardeberg, J.Y.: Spectral ordering assessment using spectral median filters. In: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing. Springer, pp. 387–397 (2015)

  36. Liao, S.X., Pawlak, M.: On image analysis by moments. IEEE Trans. Pattern Anal. Mach. Intell. 18(3), 254–266 (1996)

    Article  Google Scholar 

  37. Flusser, J., Suk, T., Boldyš, J., Zitová, B.: Projection operators and moment invariants to image blurring. IEEE Trans. Pattern Anal. Mach. Intell. 37(4), 786–802 (2015)

    Article  Google Scholar 

  38. Flusser, J., Suk, T., Zitova, B.: 2D and 3D Image Analysis by Moments. Wiley, New York (2016)

    Book  MATH  Google Scholar 

  39. Mukundan, R., Ramakrishnan, K.R.: Moment Functions in Image Analysis: Theory and Applications. World Scientific, Singapore (1998)

    Book  MATH  Google Scholar 

  40. Papakostas, G.A.: Moments and moment invariants: theory and applications. Science Gate 1, 3–32 (2014)

    Google Scholar 

  41. Bigun, J., du Buf, J.M.H.: N-folded symmetries by complex moments in Gabor space and their application to unsupervised texture segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 16(1), 80–87 (1994)

    Article  Google Scholar 

  42. Super, B.J., Bovik, A.C.: Shape from texture using local spectral moments. IEEE Trans. Pattern Anal. Mach. Intell. 17(4), 333–343 (1995)

    Article  Google Scholar 

  43. Mäenpää, T., Pietikäinen, M., Viertola, J.: Separating color and pattern information for color texture discrimination. In: Proceedings of 16th International Conference on Pattern Recognition, 2002, pp. 668–671 (2002)

  44. Liu, L., Fieguth, P., Guo, Y., Wang, X., Pietikäinen, M.: Local binary features for texture classification: taxonomy and experimental study. Pattern Recognit. 62, 135–160 (2017)

    Article  Google Scholar 

  45. Liu, L., Lao, S., Fieguth, P.W., Guo, Y., Wang, X., Pietikäinen, M.: Median robust extended local binary pattern for texture classification. IEEE Trans. Image Process. 25(3), 1368–1381 (2016)

    Article  MathSciNet  Google Scholar 

  46. Porebski, A., Vandenbroucke, N., Macaire, L., Hamad, D.: A new benchmark image test suite for evaluating colour texture classification schemes. Multimed. Tools Appl. 70(1), 543–556 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, University of Eastern Finland, P.O. Box 111, 80101, Joensuu, Finland

    Arash Mirhashemi

Authors
  1. Arash Mirhashemi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arash Mirhashemi.

Additional information

This study was funded by the Finnish Funding Agency for Innovation (TEKES), funding decision 3268/31/2015.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirhashemi, A. Introducing spectral moment features in analyzing the SpecTex hyperspectral texture database. Machine Vision and Applications 29, 415–432 (2018). https://doi.org/10.1007/s00138-017-0892-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-017-0892-9

Keywords

Search

Navigation