Improvement of Clustering for Hyperspectral Images using Spectral Information Divergence

Document Type : علمی - پژوهشی

Author

Instructor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran

Abstract

K-Means is one of the most frequently used unsupervised classification approaches for remotely sensed image analysis. In standard K-Means version, the Euclidean distance (ED) has used to estimate the dissimilarity between an unknown vector data and the cluster center. Since, this measure is very sensitive to topographic and environmental effects on spectral observations, we have proposed to replace it with a new one for goal of hyperspectral image clustering. The Spectral Information Divergence (SID) is a stochastic measure that is a more reliable dissimilarity measure when compared to ED as a deterministic measure. Where the ED measure the spectral distance between vector data and the clusters, SID models the probability distributions for vector data and clusters by normalizing their spectral signatures and measures the distances between them. This idea has applied to develop an enhanced clustering framework. The experimental results on three real hyperspectral images collected by HyMap, HYDICE and Hyperion sensors show that the proposed method improves classification results. In the manner that the Kappa coefficient of the classification results of three hyperspectral imagery datasets increased by about 7%, 56% and 10%, respectively. 

Keywords

References

  1. Adep, R.N., Vijayan, A.P., Shetty, A. & Ramesh, H., 2016, Performance evaluation of hyperspectral classification algorithms on AVIRIS mineral data, Perspectives in Science, 8, 722-726.
  2. Al-Daoud, M.B., 2007, A New Algorithm for Cluster Initialization, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 1(4), 1031-1033.
  3. Aydav, P.S.S. & Minz, S., 2014, Soft Subspace Fuzzy C-Means with Spatial Information for Clustering of Hyperspectral Images, Journal of Basic and Applied Engineering Research, 1(7), 38-42.
  4. Brereton, R.G., 1992, Multivariate Pattern Recognition in Chemometrics, Illustrated by Case Studies (Data Handling in Science and Technology, Vol. 9), Elsevier Science.
  5. Chang, C.-I., 2000, An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis, IEEE Transactions on Information Theory, 46(5), 1927-1932.
  6. Chang, C.-I., 2003, Hyperspectral Imaging: Techniques for spectral Detection and Classification, Springer US, New York.
  7. Chen, J., Jia, X., Yang, W. & Matsushita, B., 2009, Generalization of Subpixel Analysis for Hyperspectral Data with Flexibility in Spectral Similarity Measures, IEEE Transactions on Geoscience and Remote Sensing, 47(7), 2165-2171.
  8. Corless, R.M. & Jefirey, D.J., 2002, The Wright omega Function, Paper presented at the Artificial Intelligence, Automated Reasoning, and Symbolic Computation (Ed. J. Calmet, B. Benhamou, O. Caprotti, L. Henocque and V. Sorge), Berlin.
  9. Du, Y., Chang, C.-I., Ren, H., Chang, C.-C., Jensen, J.O., & D’Amico, F.M., 2004, New hyperspectral discrimination measure for spectral characterization, Society of Photo-Optical Instrumentation Engineers, 43(8), 1777-1786.
  10. Duda, R.O., Hart, P.E. & Stork, D.G., 2001, Pattern Classification, Wiley, New York.
  11. Erudel, T., Fabre, S., Houet, T., Mazier, F., & Briottet, X., 2017, Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements, Remote Sensing, 9(7), 748-806.
  12. Galal, A., Hassan, H. & Imam, I.F., 2012, A novel approach for measuring hyperspectral similarity, Applied Soft Computing, 12(10), 3115-3123.
  13. Gholizadeh, H., Gamon, J.A., Zygielbaum, A.I., Wang, R., Schweiger, A.K. & Cavender-Bares, J., 2018, Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems, Remote Sensing of Environment, 206, 240-253.
  14. Guha, S., Rastogi, R. & Shim, K., 2001, Cure: an efficient clustering algorithm for large databases, Information Systems, 26(1), 35-58.
  15. Homayouni, S., & Roux, M., 2004, Hyperspectral image Analysis for Material Mapping Using Spectral Matching, XX ISPRS Congress, Istanbul.
  16. Jain, A.K., 2010, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, 31(8), 651-666.
  17. Jain, A.K. & Dubes, R.C., 1988, Algorithms for clustering data, Prentice Hall, Englewood Cliffs, New Jersey.
  18. Jensen, J.R., 1996, Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall, Upper Saddle River, New Jersey.
  19. Jie, Y., Peihuang, G., Pinxiang, C., Zhongshan, Z. & Wenbin, R., 2008, Remote Sensing Image Classification Based on Improved Fuzzy c-Means, Geo-spatial Information Science, 11(2), 90-94.
  20. MacQueen, J.B., 1967, Some Methods for classification and Analysis of Multivariate Observations, 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley.
  21. Palsson, F., Sigurdsson, J., Sveinsson, J.R. & Ulfarsson, M. O., 2017, Neural network hyperspectral unmixing with spectral information divergence objective, Paper presented at the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth.
  22. Shi, W., 2009, Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses, CRC Press, Boca Raton.
  23. Timm, H., Borgelt, C., Döring, C., & Kruse, R., 2004, An extension to possibilistic fuzzy cluster analysis, Fuzzy Sets and Systems, 147(1), 3-16.
  24. Tou, J.T. & Gonzalez, R.C., 1974, Pattern Recognition Principles, Addison-Wesley, Massachusetts.
  25. Tran, T.N., Wehrens, R. & Buydens, L.M.C., 2003, SpaRef: a clustering algorithm for multispectral images, Analytica Chimica Acta, 490(1), 303-312.
  26. Tsai, C.-F., Wu, H.-C. & Tsai, C.-W., 2002, A new data clustering approach for data mining in large databases, International Symposium on Parallel Architectures, Algorithms and Networks, Makati.
  27. van der Meer, F., 2006, The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery, International Journal of Applied Earth Observation and Geoinformation, 8(1), 3-17.

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