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Ghassemian H, Hosseini S A. Hyper-Spectral Data Feature Extraction Using Rational Function Curve Fitting. JSDP 2016; 13 (3) :3-16
URL: http://jsdp.rcisp.ac.ir/article-1-346-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-346-en.html
Tarbiat Modares University
Abstract: (6470 Views)
In this paper, with due respect to the original data and based on the extraction of new features by smaller dimensions, a new feature reduction technique is proposed for Hyper-Spectral data classification. For each pixel of a Hyper-Spectral image, a specific rational function approximation is developed to fit its own spectral response curve (SRC) and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. The method focuses on geometrical nature of SRCs and relies on the fact that the sequence discipline - ordinance of reflectance coefficients in spectral response curve - contains some information which has not been addressed by many other existing methods based on the statistical analysis of data. Maximum likelihood classification results demonstrate that our method provides better classification accuracies in comparison with many competing feature extraction algorithms. In addition, the proposed algorithm has the possibility of being applied to all pixels of image individually and simultaneously as well.
Type of Study: Research |
Subject:
Paper
Received: 2015/03/17 | Accepted: 2015/06/14 | Published: 2017/04/23 | ePublished: 2017/04/23
Received: 2015/03/17 | Accepted: 2015/06/14 | Published: 2017/04/23 | ePublished: 2017/04/23
References
1. [1]. D. Landgrebe, Hyperspectral image data analysis as a high dimensional signal processing problem, IEEE Signal Process. Mag. 19 (2002) 17–28.
https://doi.org/10.1109/79.974718 [DOI:10.1109/79.985674]
2. [2]. B. M. Shahshahani and D. A. Landgrebe, The e®ect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon, IEEE Trans. Geosci. Remote Sens. 32 (1994) 1087–1095. [DOI:10.1109/36.312897]
3. [3]. M. Marconcini, G. Camps-Valls, and L. Bruzzone, A composite semi supervised SVM for classification of hyperspectral images, IEEE Geosci. Remote Sens. Lett. 6 (2009) 234–238. [DOI:10.1109/LGRS.2008.2009324]
4. [4]. H. Ghassemian and D. A. Landgrebe, Object-oriented feature extraction method for image data compaction, IEEE Control Syst. Mag. 8 (1988) 42–48. [DOI:10.1109/37.476]
5. [5]. G. Camps-Valls, N. Shervashidze and K. M. Borgwardt, Spatio-spectral remote sensing image classification with graph kernels, IEEE Geosci. Remote Sens. Lett. 7 (2010), 741–745. [DOI:10.1109/LGRS.2010.2046618]
6. [6]. F. Melgani and L. Bruzzone, Classification of hyperspectral remote sensing images with support vector machines, IEEE Trans. Geosci. Remote Sens. 42 (2004) 1778–1790. [DOI:10.1109/TGRS.2004.831865]
7. [7]. B.-C. Kuo and D. A. Landgrebe, Nonparametric weighted feature extraction for classification, IEEE Trans. Geosci. Remote Sens. 42 (2004) 1096–1105. [DOI:10.1109/TGRS.2004.825578]
8. [8]. R. Pu and P. Gong, Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping, Remote Sens. Environ. 91 (2004) 212–224. [DOI:10.1016/j.rse.2004.03.006]
9. [9]. J. R. Harris, D. Rogge, R. Hitchcock, O. Ijewliw and D. Wright, Mapping lithology in Canada's Arctic: Application of hyperspectral data using the minimum noise fraction transformation and matched filtering, Can. J. Earth Sci. 42 (2005) 2173–2193. [DOI:10.1139/e05-064]
10. [10]. W. Jing and I. C. Chein, Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis, IEEE Trans. Geosci. Remote Sens. 44 (2006) 1586–1600. [DOI:10.1109/TGRS.2005.863297]
11. [11]. I. Dopido, M. Zortea, A. Villa, A. Plaza and P. Gamba, Unmixing prior to supervised classification of remotely sensed hyperspectral images, IEEE Geosci. Remote Sens. Lett. 8 (2011) 760–764. [DOI:10.1109/LGRS.2011.2109367]
12. [12]. X. He, D. Cai and J. Han, Learning a maximum margin subspace for image retrieval, IEEE Trans. Knowl. Data Eng. 20 (2008) 189–201. [DOI:10.1109/TKDE.2007.190692]
13. [13]. S. Theodoridis and K. Koutroumbas, Feature selection, in Pattern Recognition, 4th edn. (Academic Press, 2009). [DOI:10.1016/B978-1-59749-272-0.50007-4]
14. [14]. L. Journaux, X. Tizon, I. Foucherot and P. Gouton, Dimensionality reduction techniques: An operational comparison on multispectral satellite images using unsupervised clustering, in Proc. 7th Nordic Signal Processing Symp. 2006, NORSIG 2006, Rejkjavik. pp. 242–245. [DOI:10.1109/NORSIG.2006.275233]
15. [15]. M. Fauvel, J. Chanussot and J. A. Benediktsson, Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas, EURASIP J. Adv. Signal Process. 2009 (2009) 783194. [DOI:10.1155/2009/783194]
16. [16]. K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic Press, 1990).
17. [17].D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing 29, Vol. 29 (John Wiley & Sons, 2003). [DOI:10.1002/0471723800]
18. [18]. S. Mika, G. Ratsch, J. Weston, B. Scholkopf and K. Muller, Fisher discriminant analysis with kernels, in Proc. 1999 IEEE Signal Processing Society Workshop: Neural Networks for Signal Processing IX, 1999, Madison, WI, August 1999, pp. 41–48. [DOI:10.1109/NNSP.1999.788121]
19. [19]. G. Baudat and F. Anouar, Generalized discriminant analysis using a kernel approach, Neural Comput. 12 (2000) 2385–2404. [DOI:10.1162/089976600300014980]
20. [20]. X. Jia, B.-C. Kuo and M. M. Crawford, Feature mining for hyperspectral image classification, Proc. IEEE 101 (2013) 676–697. [DOI:10.1109/JPROC.2012.2229082]
21. [21]. S. A. Hosseini and H. Ghassemian, Classific-ation of hyperspectral and multispectral images by using fractal dimension of spectral response curve, in 2012 20th Iranian Conf. Electrical Engineering (ICEE) (2012), pp. 1452–1457.
22. [22]. S. A. Hosseini and H. Ghassemian, A new hyperspectral image classification approach using fractal dimension of spectral response curve, in 2013 21st Iranian Conf. Electrical Engineering (ICEE) (2013), pp. 1–6.
23. [23]. M. Pal and P. M. Mather, An assessment of the effectiveness of decision tree methods for land cover classification, Remote Sens. Environ. 86 (2003) 554–565. [DOI:10.1016/S0034-4257(03)00132-9]
24. [24]. S. A. Hosseini and H. Ghassemian, A new approach to hyperspectral data compression using rational function approximation for spectral response curve fitting, in 2014 7th Int. Symp. Telecommunications (IST 2014), Tehran, Iran, pp. 844–848. [DOI:10.1109/ISTEL.2014.7000821] [PMID] [PMCID]
25. [25]. G. Baker and P. Graves-Morris, Pade Approx-imants Encyclopedia of Mathematics and its Applications, Vol. 59, (Cambridge University Press, Cambridge, 1996). [PMCID]
26. [29]. Universidad-del-Pais-Vasco, Hyperspectral remote sensing scenes (2014), http://www. ehu.es/ccwintco/index.php?title=Hyperspectral Remote Sensing Scenes.
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