Abstract

Support vector machine (SVM) is widely used in classification of hyperspectral reflectance data. In traditional SVM, features are generated from all or subsets of spectral bands with each feature contributing equally to the classification. In classification of small hyperspectral reflectance data sets, a common challenge is Hughes phenomenon, which is caused by many redundant features and resulting in subsequent poor classification accuracy. In this study, we examined two approaches to assigning weights to SVM features to increase classification accuracy and reduce adverse effects of Hughes phenomenon: 1) “RSVM” refers to support vector machine with relief feature weighting algorithm, and 2) “FRSVM” refers to support vector machine with fuzzy relief feature weighting algorithm. We used standardized weights to extract a subset of features with high classification contribution. Analyses were conducted on a reflectance data set of individual corn kernels from three inbred lines and a public data set with three selected land-cover classes. Both weighting methods and reduction of features increased classification accuracy of traditional SVM and therefore reduced adverse effects of Hughes phenomenon.

© 2011 OSA

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References

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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  6. F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Trans. Image Process. 19(11), 2983–2999 (2010).
    [CrossRef]
  7. A. M. Filippi, R. Archibald, B. L. Bhaduri, and E. A. Bright, “Hyperspectral agricultural mapping using support vector machine-based endmember extraction (SVM-BEE),” Opt. Express 17(26), 23823–23842 (2009).
    [CrossRef] [PubMed]
  8. B. Ergun, T. Kavzoglu, I. Colkesen, and C. Sahin, “Data filtering with support vector machines in geometric camera calibration,” Opt. Express 18(3), 1927–1936 (2010).
    [CrossRef] [PubMed]
  9. M. A. Kumar and M. Gopal, “A comparison study on multiple binary-class SVM methods for unilabel text categorization,” Pattern Recognit. Lett. 31(11), 1437–1444 (2010).
    [CrossRef]
  10. N. Shanthi and K. Duraiswamy, “A novel SVM-based handwritten Tamil character recognition system,” Pattern Anal. Appl. 13(2), 173–180 (2010).
    [CrossRef]
  11. X. Xu, D. Zhang, and X. Zhang, “An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine,” Opt. Appl. 39, 601–615 (2009).
  12. B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson, “Customizing kernel functions for SVM-based hyperspectral image classification,” IEEE Trans. Image Process. 17(4), 622–629 (2008).
    [CrossRef] [PubMed]
  13. J. Li, X. Gao, and L. Jiao, “A new feature weighted fuzzy cluster algorithm,” Acta. Electron. 34, 89–92 (2006).
  14. C. Nansen, A. J. Sidumo, and S. Capareda, “Variogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plants and to estimate biofuel potential,” Appl. Spectrosc. 64(6), 627–636 (2010).
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  17. K. Kira and L. A. Rendell, “A practical approach to feature selsecion,” in Proceeding of the 9th International Workshop on Machine Learning, D. Sleeman, ed. (Morgan Kaufmann, San Francisco, CA, 1992), pp. 249–256.
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    [CrossRef]
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    [CrossRef]
  22. C. Lee and D. A. Landgrebe, “Analyzing high-dimensional multispectral data,” IEEE Trans. Geosci. Remote Sens. 31(4), 792–800 (1993).
    [CrossRef]
  23. D. J. Sebald and J. A. Bucklew, “Support vector machine techniques for nonlinear equalization,” IEEE Trans. Signal Process. 48(11), 3217–3226 (2000).
    [CrossRef]
  24. C. Nansen, T. Herrman, and R. Swanson, “Machine vision detection of bonemeal in animal feed samples,” Appl. Spectrosc. 64(6), 637–643 (2010).
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  25. F. A. Mianji and Y. Zhang, “Robust hyperspectral classification using relevance vector machine,” IEEE Trans. Geosci. Remote Sens. 49(6), 2100–2112 (2011).
    [CrossRef]

2011

F. A. Mianji and Y. Zhang, “Robust hyperspectral classification using relevance vector machine,” IEEE Trans. Geosci. Remote Sens. 49(6), 2100–2112 (2011).
[CrossRef]

2010

T. Kayikcioglu and O. Aydemir, “A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data,” Pattern Recognit. Lett. 31(11), 1207–1215 (2010).
[CrossRef]

M. Pal and G. M. Foody, “Feature selection for classification of hyperspectral data by SVM,” IEEE Trans. Geosci. Remote Sens. 48(5), 2297–2307 (2010).
[CrossRef]

F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Trans. Image Process. 19(11), 2983–2999 (2010).
[CrossRef]

M. A. Kumar and M. Gopal, “A comparison study on multiple binary-class SVM methods for unilabel text categorization,” Pattern Recognit. Lett. 31(11), 1437–1444 (2010).
[CrossRef]

N. Shanthi and K. Duraiswamy, “A novel SVM-based handwritten Tamil character recognition system,” Pattern Anal. Appl. 13(2), 173–180 (2010).
[CrossRef]

B. Ergun, T. Kavzoglu, I. Colkesen, and C. Sahin, “Data filtering with support vector machines in geometric camera calibration,” Opt. Express 18(3), 1927–1936 (2010).
[CrossRef] [PubMed]

C. Nansen, A. J. Sidumo, and S. Capareda, “Variogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plants and to estimate biofuel potential,” Appl. Spectrosc. 64(6), 627–636 (2010).
[CrossRef] [PubMed]

C. Nansen, T. Herrman, and R. Swanson, “Machine vision detection of bonemeal in animal feed samples,” Appl. Spectrosc. 64(6), 637–643 (2010).
[CrossRef] [PubMed]

2009

X. Xu, D. Zhang, and X. Zhang, “An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine,” Opt. Appl. 39, 601–615 (2009).

A. M. Filippi, R. Archibald, B. L. Bhaduri, and E. A. Bright, “Hyperspectral agricultural mapping using support vector machine-based endmember extraction (SVM-BEE),” Opt. Express 17(26), 23823–23842 (2009).
[CrossRef] [PubMed]

2008

L. Wang, C. Zhao, Y. Qiao, and W. Chen, “Research on all-around weighting methods of hyperspectral imagery classification,” Int. J. Infrared Millim. Waves 27, 442–446 (2008).

B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson, “Customizing kernel functions for SVM-based hyperspectral image classification,” IEEE Trans. Image Process. 17(4), 622–629 (2008).
[CrossRef] [PubMed]

2007

P.-H. Hsu, “Feature extraction of hyperspectral images using wavelet and matching pursuit,” ISPRS J. Photogramm. Remote Sens. 62(2), 78–92 (2007).
[CrossRef]

2006

J. Li, X. Gao, and L. Jiao, “A new feature weighted fuzzy cluster algorithm,” Acta. Electron. 34, 89–92 (2006).

2004

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machine,” IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004).
[CrossRef]

2000

D. J. Sebald and J. A. Bucklew, “Support vector machine techniques for nonlinear equalization,” IEEE Trans. Signal Process. 48(11), 3217–3226 (2000).
[CrossRef]

1995

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[CrossRef]

1993

C. Lee and D. A. Landgrebe, “Analyzing high-dimensional multispectral data,” IEEE Trans. Geosci. Remote Sens. 31(4), 792–800 (1993).
[CrossRef]

1981

L. R. LaMotte and A. McWhorter, “A regression-based linear classification procedure,” Educ. Psychol. Meas. 41(2), 341–347 (1981).
[CrossRef]

1960

J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
[CrossRef]

Archibald, R.

Aydemir, O.

T. Kayikcioglu and O. Aydemir, “A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data,” Pattern Recognit. Lett. 31(11), 1207–1215 (2010).
[CrossRef]

Bhaduri, B. L.

Bovolo, F.

F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Trans. Image Process. 19(11), 2983–2999 (2010).
[CrossRef]

Bright, E. A.

Bruzzone, L.

F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Trans. Image Process. 19(11), 2983–2999 (2010).
[CrossRef]

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machine,” IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004).
[CrossRef]

Bucklew, J. A.

D. J. Sebald and J. A. Bucklew, “Support vector machine techniques for nonlinear equalization,” IEEE Trans. Signal Process. 48(11), 3217–3226 (2000).
[CrossRef]

Capareda, S.

Carlin, L.

F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Trans. Image Process. 19(11), 2983–2999 (2010).
[CrossRef]

Chen, W.

L. Wang, C. Zhao, Y. Qiao, and W. Chen, “Research on all-around weighting methods of hyperspectral imagery classification,” Int. J. Infrared Millim. Waves 27, 442–446 (2008).

Cohen, J.

J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
[CrossRef]

Colkesen, I.

Cortes, C.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[CrossRef]

Damper, R. I.

B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson, “Customizing kernel functions for SVM-based hyperspectral image classification,” IEEE Trans. Image Process. 17(4), 622–629 (2008).
[CrossRef] [PubMed]

Duraiswamy, K.

N. Shanthi and K. Duraiswamy, “A novel SVM-based handwritten Tamil character recognition system,” Pattern Anal. Appl. 13(2), 173–180 (2010).
[CrossRef]

Ergun, B.

Filippi, A. M.

Foody, G. M.

M. Pal and G. M. Foody, “Feature selection for classification of hyperspectral data by SVM,” IEEE Trans. Geosci. Remote Sens. 48(5), 2297–2307 (2010).
[CrossRef]

Gao, X.

J. Li, X. Gao, and L. Jiao, “A new feature weighted fuzzy cluster algorithm,” Acta. Electron. 34, 89–92 (2006).

Gopal, M.

M. A. Kumar and M. Gopal, “A comparison study on multiple binary-class SVM methods for unilabel text categorization,” Pattern Recognit. Lett. 31(11), 1437–1444 (2010).
[CrossRef]

Gunn, S. R.

B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson, “Customizing kernel functions for SVM-based hyperspectral image classification,” IEEE Trans. Image Process. 17(4), 622–629 (2008).
[CrossRef] [PubMed]

Guo, B.

B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson, “Customizing kernel functions for SVM-based hyperspectral image classification,” IEEE Trans. Image Process. 17(4), 622–629 (2008).
[CrossRef] [PubMed]

Herrman, T.

Hsu, P.-H.

P.-H. Hsu, “Feature extraction of hyperspectral images using wavelet and matching pursuit,” ISPRS J. Photogramm. Remote Sens. 62(2), 78–92 (2007).
[CrossRef]

Jiao, L.

J. Li, X. Gao, and L. Jiao, “A new feature weighted fuzzy cluster algorithm,” Acta. Electron. 34, 89–92 (2006).

Kavzoglu, T.

Kayikcioglu, T.

T. Kayikcioglu and O. Aydemir, “A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data,” Pattern Recognit. Lett. 31(11), 1207–1215 (2010).
[CrossRef]

Kumar, M. A.

M. A. Kumar and M. Gopal, “A comparison study on multiple binary-class SVM methods for unilabel text categorization,” Pattern Recognit. Lett. 31(11), 1437–1444 (2010).
[CrossRef]

LaMotte, L. R.

L. R. LaMotte and A. McWhorter, “A regression-based linear classification procedure,” Educ. Psychol. Meas. 41(2), 341–347 (1981).
[CrossRef]

Landgrebe, D. A.

C. Lee and D. A. Landgrebe, “Analyzing high-dimensional multispectral data,” IEEE Trans. Geosci. Remote Sens. 31(4), 792–800 (1993).
[CrossRef]

Lee, C.

C. Lee and D. A. Landgrebe, “Analyzing high-dimensional multispectral data,” IEEE Trans. Geosci. Remote Sens. 31(4), 792–800 (1993).
[CrossRef]

Li, J.

J. Li, X. Gao, and L. Jiao, “A new feature weighted fuzzy cluster algorithm,” Acta. Electron. 34, 89–92 (2006).

McWhorter, A.

L. R. LaMotte and A. McWhorter, “A regression-based linear classification procedure,” Educ. Psychol. Meas. 41(2), 341–347 (1981).
[CrossRef]

Melgani, F.

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machine,” IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004).
[CrossRef]

Mianji, F. A.

F. A. Mianji and Y. Zhang, “Robust hyperspectral classification using relevance vector machine,” IEEE Trans. Geosci. Remote Sens. 49(6), 2100–2112 (2011).
[CrossRef]

Nansen, C.

Nelson, J. B.

B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson, “Customizing kernel functions for SVM-based hyperspectral image classification,” IEEE Trans. Image Process. 17(4), 622–629 (2008).
[CrossRef] [PubMed]

Pal, M.

M. Pal and G. M. Foody, “Feature selection for classification of hyperspectral data by SVM,” IEEE Trans. Geosci. Remote Sens. 48(5), 2297–2307 (2010).
[CrossRef]

Qiao, Y.

L. Wang, C. Zhao, Y. Qiao, and W. Chen, “Research on all-around weighting methods of hyperspectral imagery classification,” Int. J. Infrared Millim. Waves 27, 442–446 (2008).

Sahin, C.

Sebald, D. J.

D. J. Sebald and J. A. Bucklew, “Support vector machine techniques for nonlinear equalization,” IEEE Trans. Signal Process. 48(11), 3217–3226 (2000).
[CrossRef]

Shanthi, N.

N. Shanthi and K. Duraiswamy, “A novel SVM-based handwritten Tamil character recognition system,” Pattern Anal. Appl. 13(2), 173–180 (2010).
[CrossRef]

Sidumo, A. J.

Swanson, R.

Vapnik, V.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[CrossRef]

Wang, L.

L. Wang, C. Zhao, Y. Qiao, and W. Chen, “Research on all-around weighting methods of hyperspectral imagery classification,” Int. J. Infrared Millim. Waves 27, 442–446 (2008).

Xu, X.

X. Xu, D. Zhang, and X. Zhang, “An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine,” Opt. Appl. 39, 601–615 (2009).

Zhang, D.

X. Xu, D. Zhang, and X. Zhang, “An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine,” Opt. Appl. 39, 601–615 (2009).

Zhang, X.

X. Xu, D. Zhang, and X. Zhang, “An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine,” Opt. Appl. 39, 601–615 (2009).

Zhang, Y.

F. A. Mianji and Y. Zhang, “Robust hyperspectral classification using relevance vector machine,” IEEE Trans. Geosci. Remote Sens. 49(6), 2100–2112 (2011).
[CrossRef]

Zhao, C.

L. Wang, C. Zhao, Y. Qiao, and W. Chen, “Research on all-around weighting methods of hyperspectral imagery classification,” Int. J. Infrared Millim. Waves 27, 442–446 (2008).

Acta. Electron.

J. Li, X. Gao, and L. Jiao, “A new feature weighted fuzzy cluster algorithm,” Acta. Electron. 34, 89–92 (2006).

Appl. Spectrosc.

Educ. Psychol. Meas.

J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
[CrossRef]

L. R. LaMotte and A. McWhorter, “A regression-based linear classification procedure,” Educ. Psychol. Meas. 41(2), 341–347 (1981).
[CrossRef]

IEEE Trans. Geosci. Remote Sens.

M. Pal and G. M. Foody, “Feature selection for classification of hyperspectral data by SVM,” IEEE Trans. Geosci. Remote Sens. 48(5), 2297–2307 (2010).
[CrossRef]

F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machine,” IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004).
[CrossRef]

C. Lee and D. A. Landgrebe, “Analyzing high-dimensional multispectral data,” IEEE Trans. Geosci. Remote Sens. 31(4), 792–800 (1993).
[CrossRef]

F. A. Mianji and Y. Zhang, “Robust hyperspectral classification using relevance vector machine,” IEEE Trans. Geosci. Remote Sens. 49(6), 2100–2112 (2011).
[CrossRef]

IEEE Trans. Image Process.

B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson, “Customizing kernel functions for SVM-based hyperspectral image classification,” IEEE Trans. Image Process. 17(4), 622–629 (2008).
[CrossRef] [PubMed]

F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Trans. Image Process. 19(11), 2983–2999 (2010).
[CrossRef]

IEEE Trans. Signal Process.

D. J. Sebald and J. A. Bucklew, “Support vector machine techniques for nonlinear equalization,” IEEE Trans. Signal Process. 48(11), 3217–3226 (2000).
[CrossRef]

Int. J. Infrared Millim. Waves

L. Wang, C. Zhao, Y. Qiao, and W. Chen, “Research on all-around weighting methods of hyperspectral imagery classification,” Int. J. Infrared Millim. Waves 27, 442–446 (2008).

ISPRS J. Photogramm. Remote Sens.

P.-H. Hsu, “Feature extraction of hyperspectral images using wavelet and matching pursuit,” ISPRS J. Photogramm. Remote Sens. 62(2), 78–92 (2007).
[CrossRef]

Mach. Learn.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[CrossRef]

Opt. Appl.

X. Xu, D. Zhang, and X. Zhang, “An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine,” Opt. Appl. 39, 601–615 (2009).

Opt. Express

Pattern Anal. Appl.

N. Shanthi and K. Duraiswamy, “A novel SVM-based handwritten Tamil character recognition system,” Pattern Anal. Appl. 13(2), 173–180 (2010).
[CrossRef]

Pattern Recognit. Lett.

T. Kayikcioglu and O. Aydemir, “A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data,” Pattern Recognit. Lett. 31(11), 1207–1215 (2010).
[CrossRef]

M. A. Kumar and M. Gopal, “A comparison study on multiple binary-class SVM methods for unilabel text categorization,” Pattern Recognit. Lett. 31(11), 1437–1444 (2010).
[CrossRef]

Other

B. E. Boser, I. M. Guyon, and V. Vapnik, “A training algorithm for optimal margin classifiers,” in COLT '92 Proceedings of the fifth annual workshop on computational learning theory, D. Haussler, ed. (ACM, New York, NY, 1992), pp. 144–152.

V. Vapnik, The Nature of Statistical Learning Theory (Springer & New York, 2000), Chap. 1.

L. Gao, F. Gao, X. Guan, D. Zhou, and J. Li, “A regression algorithm based on AdaBoost,” in WCICA 2006: Sixth World Congress on Intelligent Control and Automation, D. M. Zhou, ed. (IEEE Computer Society Press, Dalian, Liaoning, 2006), pp. 4400–4404.

K. Kira and L. A. Rendell, “A practical approach to feature selsecion,” in Proceeding of the 9th International Workshop on Machine Learning, D. Sleeman, ed. (Morgan Kaufmann, San Francisco, CA, 1992), pp. 249–256.

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Figures (6)

Fig. 1
Fig. 1

Digital image of the corn genotypes: Wild type (left column), Mutant 1 (center column), and Mutant 2 (right column) (a), and corresponding average reflectance profiles (b).

Fig. 2
Fig. 2

Pseudo-color image of AVIRIS data set (composed of band 17, 27 and 57) (a), and corresponding average reflectance profiles (b).

Fig. 3
Fig. 3

Cross validation of corn kernel data set (a), cross validation of AVIRIS data set (b)

Fig. 4
Fig. 4

Comparison of standardized weights obtained by using two different weighting methods: support vector machine with relief feature weighting algorithm (RSVM) and support vector machine with fuzzy relief feature weighting algorithm (FRSVM) on corn kernel data set (a), and the ratio of standardized weights using FRSVM and RSVM (b). Parameter L was equal to 1.

Fig. 5
Fig. 5

Comparison of standardized weights obtained by using two different weighting methods: support vector machine with relief feature weighting algorithm (RSVM) and support vector machine with fuzzy relief feature weighting algorithm (FRSVM) on AVIRIS data set (a), and the ratio of standardized weights using FRSVM and RSVM (b). Parameter L was equal to 1.

Fig. 6
Fig. 6

Comparison of overall accuracies (%) conducted by RSVM and FRSVM algorithm with different number of features using corn kernel data (a) and AVIRIS data (b). 1/10 of the original data was selected as training data set. Parameter L was equal to 1.

Tables (4)

Tables Icon

Table 1 Comparison of classification accuracies (%), overall accuracies (%) and Cohen Kappa coefficients conducted by the SVM, RSVM and FRSVM algorithm yielded on corn kernel data set

Tables Icon

Table 2 Comparison of classification accuracies (%), overall accuracies (%) and Cohen Kappa coefficients conducted by the SVM, RSVM and FRSVM algorithm yielded on AVIRIS data set

Tables Icon

Table 3 Difference between peak accuracy and that derived from the use of all 160 features acquired from corn kernel data

Tables Icon

Table 4 Difference between peak accuracy and that derived from the use of all 200 features acquired from AVIRIS data

Metrics