Combined FATEMD-based band selection method for hyperspectral images

Combined FATEMD-based band selection method for hyperspectral images

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Feature selection, which is called band selection for hyperspectral data, is widely used for hyperspectral images. A novel hyperspectral band selection method based on combined fast and adaptive tridimensional empirical mode decomposition (cFATEMD) is proposed in this study. The hyperspectral data is decomposed into a set of tridimensional intrinsic mode functions (TIMFs) and a residual (RES) by FATEMD, which can reduce high-frequency noise and signal. A stop condition of the decomposition is proposed based on the k-means clustering algorithm and the Dunn validity index, which can prevent excessive decomposition and make generated RES contain as much useful information as possible. In consideration of the useful information in decomposition results, these TIMFs and the RES are combined into a new data based on the spectral similarity between themselves and the original data. Four state-of-the-art band selection methods, cooperating with the proposed cFATEMD, are used to select bands by the new combined data. Several experiments are conducted on three publicly available hyperspectral datasets and the results are compared with corresponding methods’ results using the original data. Experimental results demonstrate that the proposed method yields great classification appearance.


    1. 1)
      • 1. Chen, G., Li, C., Sun, W.: ‘Hyperspectral face recognition via feature extraction and CRC-based classifier’, IET Image Process., 2017, 11, (4), pp. 266272.
    2. 2)
      • 2. Wang, Y., Xue, J.: ‘Matched shrunken subspace detectors for hyperspectral target detection’, Neurocomputing, 2018, 272, pp. 226236.
    3. 3)
      • 3. Imani, M., Ghassemian, H.: ‘Edge patch image-based morphological profiles for classification of multispectral and hyperspectral data’, IET Image Process., 2016, 11, (3), pp. 164172.
    4. 4)
      • 4. Qiao, T., Ren, J., Sun, M., et al: ‘Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications’, Int. J. Remote Sens., 2014, 35, (20), pp. 73167337.
    5. 5)
      • 5. Chen, C., Jiang, F., Yang, C., et al: ‘Hyperspectral classification based on spectral-spatial convolutional neural networks’, Eng. Appl. Artif. Intell., 2018, 68, pp. 165171.
    6. 6)
      • 6. Zhao, J., Zhou, Y., Jia, T., et al: ‘Spectral-spatial classification of hyperspectral imagery with cooperative game’, ISPRS J. Photogramm. Remote Sens., 2018, 135, pp. 3142.
    7. 7)
      • 7. Guan, L., Xie, W., Pei, J.: ‘Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images’, Pattern Recognit., 2015, 48, (10), pp. 32163226.
    8. 8)
      • 8. Ahmad, M., Khan, A.M., Hussain, R.: ‘Graph-based spatial-spectral feature learning for hyperspectral image classification’, IET Image Process., 2017, 11, (12), pp. 13101316.
    9. 9)
      • 9. Zabalza, J., Ren, J., Yang, M., et al: ‘Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing’, ISPRS J. Photogramm. Remote Sens., 2014, 93, (7), pp. 112122.
    10. 10)
      • 10. Wang, Y., Huang, S., Liu, Z., et al: ‘Locality preserving projection based on endmember extraction for hyperspectral image dimensionality reduction and target detection’, Appl. Spectrosc., 2016, 70, (9), pp. 15731581.
    11. 11)
      • 11. Ren, J., Zabalza, J., Marshall, S., et al: ‘Effective feature extraction and data reduction in remote sensing using hyperspectral imaging’, IEEE Signal Process. Mag., 2014, 31, (4), pp. 149154.
    12. 12)
      • 12. Zhang, S.: ‘Studies of high spectral resolution atmospheric sounding data compression and noise reduction based on principal component analysis method’. Proc. Int. Congress on Image and Signal, Tianjin, China, October 2009, pp. 15.
    13. 13)
      • 13. Sun, J., Liu, Y.: ‘The fusion arithmetic of multi-resolution remote sense image based on a modified fast independent component analysis’. Proc. Synthetic Aperture Radar, Huangshan, China, November 2007, pp. 342346.
    14. 14)
      • 14. Jin, X., Scott, P., Harold, C.: ‘A comparative study of target detection algorithms for hyperspectral imagery’. Int. Society for Optical Engineering, Orlando, FL, USA, April 2009, pp. 112.
    15. 15)
      • 15. Fauvel, M., Chanussot, J., Benediktsson, J.A.: ‘Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas’, EURASIP J. Adv. Signal Process., 2009, 2009, (1), pp. 114.
    16. 16)
      • 16. Zhao, C., Wang, Y., Mei, F.: ‘Kernel ICA feature extraction for anomaly detection in hyperspectral imagery’, Chin. J. Electron., 2012, 21, (2), pp. 265269.
    17. 17)
      • 17. Yang, G., Liu, H., Yu, X.: ‘Hyperspectral remote sensing image classification based on kernel Fisher discriminant analysis’. Proc. 2007 Int. Conf. Wavelet Analysis and Pattern Recognition, Beijing, China, November 2008, pp. 11391143.
    18. 18)
      • 18. Belkin, M., Niyogi, Y.: ‘Laplacian eigenmaps for dimensionality reduction and data representation’, Neural Comput., 2006, 15, (6), pp. 13731396.
    19. 19)
      • 19. Roweis, S.T., Saul, L.K.: ‘Nonlinear dimensionality reduction by locally linear embedding’, Science, 2000, 290, (5500), pp. 23232326.
    20. 20)
      • 20. Zhang, Z., Zhang, H.: ‘Nonlinear dimension reduction via local tangent space alignment’. Lecture Notes in Computer Science, Hong Kong, China, March 2003, pp. 477481.
    21. 21)
      • 21. Zabalza, J., Ren, J., Ren, J.: ‘Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging’, Opt. Soc., 2014, 53, (20), pp. 44404449.
    22. 22)
      • 22. Yang, C., Tan, Y., Lorenzo, B., et al: ‘Discriminative feature metric learning in the affinity propagation model for band selection in hyperspectral images’, Remote Sens., 2017, 9, (8), pp. 116.
    23. 23)
      • 23. Cao, X., Li, X., Li, X., et al: ‘Hyperspectral band selection with objective image quality assessment’, Int. J. Remote Sens., 2017, 38, (12), pp. 36563668.
    24. 24)
      • 24. Yang, R., Su, L., Zhao, X., et al: ‘Representative band selection for hyperspectral image classification’, J. Vis. Commun. Image Represent., 2017, 48, pp. 396403.
    25. 25)
      • 25. Zeng, X., Durrani, T.S.: ‘Band selection for hyperspectral images using copulas-based mutual information’. IEEE Workshop on Statistical Signal Processing Proc., Cardiff, UK, August 2009, pp. 341344.
    26. 26)
      • 26. Sun, W., Tian, L., Xu, Y., et al: ‘Fast and robust self-representation method for hyperspectral band selection’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, 10, (11), pp. 50875098.
    27. 27)
      • 27. Fleuret, F.: ‘Fast binary feature selection with conditional mutual information’, J. Mach. Learn. Res., 2004, 5, (3), pp. 15311555.
    28. 28)
      • 28. Santos, L.C.B.D., Guimaraes, S.J.F., Santos, J.A.D., et al: ‘Efficient unsupervised band selection through spectral rhythms’, IEEE. J. Sel. Top. Signal. Process., 2017, 9, (6), pp. 10161025.
    29. 29)
      • 29. Yang, C., Liu, S., Bruzzone, L., et al: ‘A semisupervised feature metric based band selection method for hyperspectral image classification’. Workshop on Hyperspectral Image and Signal Processing, Shanghai, China, June 2012, pp. 14.
    30. 30)
      • 30. Cao, X., Wei, C., Han, J., et al: ‘Hyperspectral band selection using improved classification map’. IEEE Geosci. Remote Sens. Lett., 2017, 14, (11), pp. 21472151.
    31. 31)
      • 31. Peng, H., Long, F., Ding, C.: ‘Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy’. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (8), pp. 12261238.
    32. 32)
      • 32. Jia, J., Yang, N., Zhang, C., et al: ‘Object-oriented feature selection of high spatial resolution images using an improved relief algorithm’, Math. Comput. Model., 2013, 58, (3–4), pp. 619626.
    33. 33)
      • 33. Sun, K., Geng, X., Ji, L., et al: ‘A new band selection method for hyperspectral image based on data quality’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (6), pp. 26972703.
    34. 34)
      • 34. Su, H., Du, Q., Chen, G., et al: ‘Optimized hyperspectral band selection using particle swarm optimization’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (6), pp. 26592670.
    35. 35)
      • 35. Ghosh, A., Datta, A., Ghosh, S., et al: ‘Self-adaptive differential evolution for feature selection in hyperspectral image data’, Appl. Soft Comput. J., 2013, 13, (4), pp. 19691977.
    36. 36)
      • 36. Li, Z., Jing, Z., Xia, L., et al: ‘A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine’. Int. Society for Optical Engineering, Guangzhou, China, June 2008, pp. 19.
    37. 37)
      • 37. Huang, N.E., Shen, Z., Long, S.R., et al: ‘The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’, Proc. Math. Phys. Eng. Sci., 1998, 454, (1971), pp. 903995.
    38. 38)
      • 38. He, Z., Li, J., Liu, L., et al: ‘Three-dimensional empirical mode decomposition (TEMD): a fast approach motivated by separable filters’, Signal Process., 2017, 131, pp. 307319.
    39. 39)
      • 39. Nunes, J.C., Bouaoune, Y., Delechelle, E., et al: ‘Image analysis by bidimensional empirical mode decomposition’, Image Vis. Comput., 2003, 21, (12), pp. 10191026.
    40. 40)
      • 40. Bhuiyan, S.M.A., Adhami, R.R., Khan, J.F.: ‘Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation’, EURASIP J. Adv. Signal Process., 2008, 2008, (1), pp. 118.
    41. 41)
      • 41. Riffi, J., Mahraz, A.M., Abbad, A., et al: ‘3D extension of the fast and adaptive bidimensional empirical mode decomposition’, Multidimens. Syst. Signal Process., 2015, 26, (3), pp. 823834.
    42. 42)
      • 42. Feng, J., Jiao, L., Liu, F., et al: ‘Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images’. Pattern Recognit., 2016, 51, pp. 295309.

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