Advertisement

Multimedia Systems

, Volume 23, Issue 1, pp 95–104 | Cite as

Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine

  • Jun Liu
  • Xiran Zhou
  • Junyi Huang
  • Shuguang LiuEmail author
  • Huali Li
  • Shan Wen
  • Junchen Liu
Special Issue Paper

Abstract

Accurate hyperspectral image classification requires not only image features but also semantic concept. Similarity and relevance relation are both key factors in building image features and semantic measurement. To perform hyperspectral image classification from the viewpoint of semantic, this study focuses on creating a semantic annotation-based image classification method with relevance and similarity measurement. First, the computational model of relevance vector machine is utilized to perform cluster computation for hyperspectral image data. Then multi-distance learning algorithm is optimized as holding capability for multiple dimensions data. The proposed multi-distance learning algorithm with multiple dimensions is used to measure the similarity, according to the result of cluster computation through relevance vector machine. Finally, semantic annotation is introduced to complete classification of hyperspectral image with semantic concept. Validation with the ground truth data illustrates that the proposed method can provide more accurate and integrated classification result compared with the other methodologies. Therefore, the integration of similarity and relevance measurement is able to improve the performance of hyperspectral image classification.

Keywords

Semantic classification Hyperspectral image Relevance vector machine Multi-distance learning with multiple dimensions 

Notes

Acknowledgments

This work is jointly supported by the International Science and Technology Collaboration Project of China (2010DFA92720-24), National Natural Science Foundation program (No. 41301403 and No. 41471340); Chongqing Basic and Advanced Research General Project (No. cstc2013jcyjA40010); Hunan Provincial Natural Science Foundation of China (No. S2013J504B). The authors of this paper would also like to appreciate Prof. Paolo Gamba for his kindly providing hyperspectral image data of Pavia University, Pavia, northern Italy.

References

  1. 1.
    Kavouras, M., Kokla, M.: A method for the formalization and integration of geographical classifications. Int J Geogr Inf Sci 16(5), 439–445 (2002)CrossRefGoogle Scholar
  2. 2.
    Cetin, M., Musaoglu, N.: Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis. Int J Remote Sens 30(7), 1779–1804 (2009)CrossRefGoogle Scholar
  3. 3.
    Tsai, F., Lai, J.S.: Feature extraction of hyperspectral image cubes using three-dimensional gray-level cooccurrence. IEEE Trans Geosci Remote Sens 51(6), 3504–3513 (2013)CrossRefGoogle Scholar
  4. 4.
    Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit 43(7), 2367–2379 (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Hou, B., Zhang, X.R., Ye, Q., Zheng, Y.G.: A novel method for hyperspectral image classification based on Laplacian Eigenmap pixels distribution-flow. IEEE J Sel Topics Appl Earth Obs Remote Sens 6(3), 1602–1618 (2013)CrossRefGoogle Scholar
  6. 6.
    Plaza, A.J.: Parallel processing of remotely sensed hyperspectral imagery: full-pixel versus mixed-pixel classification. Concurr Comput Pract Exp 20(13), 1539–1572 (2008)CrossRefGoogle Scholar
  7. 7.
    Luo, B., Chanussot, J.: Supervised hyperspectral image classification based on spectral unmixing and geometrical features. J Signal Process Syst Signal Image Video Technol 65(3), 457–468 (2011)CrossRefGoogle Scholar
  8. 8.
    Ji, R.R., Gao, Y., Hong, R.C., Liu, Q., Tao, D.C., Li, X.L.: Spectral-spatial constraint hyperspectral image classification. IEEE Trans Geosci Remote Sens 52(3), 1811–1824 (2014)CrossRefGoogle Scholar
  9. 9.
    Zhong, P., Wang, R.S.: Learning conditional random fields for classification of hyperspectral images. IEEE Trans Image Process 19(7), 1890–1907 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ul, H., Qazi, S., Tao, L.M., Sun, F.C., Yang, S.Q.: A fast and robust sparse approach for hyperspectral data classification using a few labeled samples. IEEE Trans Geosci Remote Sens 50(6), 2287–2302 (2012)CrossRefGoogle Scholar
  11. 11.
    Qian, Y.T., Ye, M.C., Zhou, J.: Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans Geosci Remote Sens 51(4), 2276–2291 (2013)CrossRefGoogle Scholar
  12. 12.
    Zhang, L., Han, Y., Yang, Y., et al.: Discovering discriminative graphlets for aerial image categories recognition. IEEE Trans Image Process 22(12), 5071–5084 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zhang, L., Gao, Y., Xia, Y., et al.: A fine-grained image categorization system by cellet-encoded spatial pyramid modeling. IEEE Trans Ind Electron 99, 1 (2014)Google Scholar
  14. 14.
    Zhang, L., Yang, Y., Gao, Y., Yu, Y., Wang, C., Li, X.: A probabilistic associative model for segmenting weakly-supervised images. IEEE Trans Image Process 23(9), 4150–4159 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zhang, L., Gao, Y., Hong, C., et al.: Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE Trans Cybern 44(8), 1408–1419 (2014)CrossRefGoogle Scholar
  16. 16.
    Zhang, L., Song, M., Liu, X., et al.: Recognizing architecture styles by hierarchical sparse coding of blocklets. Inf Sci 254, 141–154 (2014)CrossRefGoogle Scholar
  17. 17.
    Zhang, L., Song, M., Liu, X., et al.: Fast multi-view segment graph kernel for object classification. Sig Process 93(6), 1597–1607 (2013)CrossRefGoogle Scholar
  18. 18.
    Zhang, L., Gao, Y., Lu, K., et al.: Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Trans Multimed 16(2), 470–479 (2014)CrossRefGoogle Scholar
  19. 19.
    Van Gemert, J.C., Snoek, C.G.M., et al.: Comparing compact codebooks for visual classification. Comput Vis Image Underst 114(4), 450–462 (2010)CrossRefGoogle Scholar
  20. 20.
    Hjørland, B.C., Sejer, F.: Work tasks and socio-cognitive relevance: a specific example. J Am Soc Inform Sci Technol 53(11), 960–965 (2002)CrossRefGoogle Scholar
  21. 21.
    Mianji, F.A., Zhang, Y.: Robust hyperspectral classification using relevance vector machine. IEEE Trans Geosci Remote Sens 49(6), 2100–2112 (2011)CrossRefGoogle Scholar
  22. 22.
    Pal, M., Foody, G.M.: Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data. IEEE J Sel Topics Appl Earth Obs Remote Sens 5(5), 1344–1355 (2012)CrossRefGoogle Scholar
  23. 23.
    Foody, G.M.: RVM-based multi-class classification of remotely sensed data. Int J Remote Sens 29(6), 1817–1823 (2008)CrossRefGoogle Scholar
  24. 24.
    Huang, X., Zhang, L.P.: An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens 51(1), 257–272 (2013)CrossRefGoogle Scholar
  25. 25.
    Ma, Y.Y., Zhu, L.P.: A review on dimension reduction. Int Stat Rev 81(1), 134–150 (2013)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Zhu, Y.X., Varshney, K.P., Chen, H.: ICA-based fusion for colour display of hyperspectral images. Int J Remote Sens 32(9), 2427–2450 (2011)CrossRefGoogle Scholar
  27. 27.
    Chang, C.I., Du, Q.: Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans Geosci Remote Sens 42(3), 608–619 (2004)CrossRefGoogle Scholar
  28. 28.
    Tousch, A.M., Stephane, S.H., Audibert, J.Y.: Semantic hierarchies for image annotation: a survey. Pattern Recognit 45(1), 333–345 (2012)CrossRefGoogle Scholar
  29. 29.
    Wang, M., Wan, Q.M., Gu, L.B., Song, T.Y.: Remote-sensing image retrieval by combining image visual and semantic features. Int J Remote Sens 34(12), 4200–4223 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jun Liu
    • 1
  • Xiran Zhou
    • 3
  • Junyi Huang
    • 4
  • Shuguang Liu
    • 2
    Email author
  • Huali Li
    • 5
  • Shan Wen
    • 6
  • Junchen Liu
    • 7
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqingChina
  3. 3.School of Geographical Sciences and Urban PlanningArizona State UniversityTempeUSA
  4. 4.Department of GeographyHong Kong Baptist UniversityHong KongChina
  5. 5.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  6. 6.Yunnan Electronic Computing CenterKunmingChina
  7. 7.Tianjin Institute of Surveying and MappingTianjinChina

Personalised recommendations