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Hyperspectral Image: Fundamentals and Advances

  • V. SowmyaEmail author
  • K. P. Soman
  • M. Hassaballah
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 804)

Abstract

Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more efficient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classification techniques and the most recently popular classification algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter.

References

  1. 1.
    Thenkabail, P.S., Lyon, J.G.: Hyperspectral Remote Sensing of Vegetation. CRC Press (2016)Google Scholar
  2. 2.
    Manolakis, D., Shaw, G.: Detection algorithms for hyperspectral imaging applications. IEEE Signal Process. Mag. 19(1), 29–43 (2002)CrossRefGoogle Scholar
  3. 3.
    Pohl, C., van Genderen, J.: Remote Sensing Image Fusion: A Practical Guide. CRC Press (2016)Google Scholar
  4. 4.
    Deng, Y.J., Li, H.C., Pan, L., Shao, L.Y., Du, Q., Emery, W.J.: Modified tensor locality preserving projection for dimensionality reduction of hyperspectral images. IEEE Geosci. Remote Sens. Lett. (2018)Google Scholar
  5. 5.
    Du, Q., Fowler, J.E.: Low-complexity principal component analysis for hyperspectral image compression. Int. J. High Perform. Comput. Appl. 22(4), 438–448 (2008)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Chang, C.I.: Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans. Geosci. Remote Sens. 44(6), 1586–1600 (2006)Google Scholar
  7. 7.
    Vakalopoulou, M., Platias, C., Papadomanolaki, M., Paragios, N., Karantzalos, K.: Simultaneous registration, segmentation and change detection from multisensor, multitemporal satellite image pairs. In: IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), pp. 1827–1830. IEEE (2016)Google Scholar
  8. 8.
    Ferraris, V., Dobigeon, N., Wei, Q., Chabert, M.: Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach. IEEE Trans. Geosci. Remote Sens. 56(3), 1566–1578 (2018)CrossRefGoogle Scholar
  9. 9.
    ElMasry, G., Kamruzzaman, M., Sun, D.W., Allen, P.: Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit. Rev. Food Sci. Nutr. 52(11), 999–1023 (2012)CrossRefGoogle Scholar
  10. 10.
    Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O.L., Blasco, J.: Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol. 5(4), 1121–1142 (2012)CrossRefGoogle Scholar
  11. 11.
    Xiong, Z., Sun, D.W., Zeng, X.A., Xie, A.: Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: a review. J. Food Eng. 132, 1–13 (2014)CrossRefGoogle Scholar
  12. 12.
    Kerekes, J.P., Schott, J.R.: Hyperspectral imaging systems. Hyperspectral Data Exploit. Theory Appl. 19–45 (2007)Google Scholar
  13. 13.
    Liang, H.: Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Appl. Phys. A 106(2), 309–323 (2012)CrossRefGoogle Scholar
  14. 14.
    Fischer, C., Kakoulli, I.: Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Stud. Conserv. 51, 3–16 (2006)CrossRefGoogle Scholar
  15. 15.
    Du, Q., Yang, H.: Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geosci. Remote Sens. Lett. 5(4), 564–568 (2008)CrossRefGoogle Scholar
  16. 16.
    Chang, N.B., Vannah, B., Yang, Y.J.: Comparative sensor fusion between hyperspectral and multispectral satellite sensors for monitoring microcystin distribution in lake erie. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2426–2442 (2014)CrossRefGoogle Scholar
  17. 17.
    Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., Chanussot, J.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. 1(2), 6–36 (2013)CrossRefGoogle Scholar
  18. 18.
    Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, S110–S122 (2009)CrossRefGoogle Scholar
  19. 19.
    Bhabatosh, C., et al.: Digital Image Processing and Analysis. PHI Learning Pvt, Ltd (2011)Google Scholar
  20. 20.
    Bankman, I.: Handbook of Medical Image Processing and Analysis. Elsevier (2008)Google Scholar
  21. 21.
    Bendoumi, M.A., He, M., Mei, S.: Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing. IEEE Trans. Geosci. Remote Sens. 52(10), 6574–6583 (2014)CrossRefGoogle Scholar
  22. 22.
    Akgun, T., Altunbasak, Y., Mersereau, R.M.: Super-resolution reconstruction of hyperspectral images. IEEE Trans. Image Process. 14(11), 1860–1875 (2005)CrossRefGoogle Scholar
  23. 23.
    Amro, I., Mateos, J., Vega, M., Molina, R., Katsaggelos, A.K.: A survey of classical methods and new trends in pansharpening of multispectral images. EURASIP J. Adv. Signal Process. 2011(1), 79 (2011)CrossRefGoogle Scholar
  24. 24.
    Eismann, M.T., Hardie, R.C.: Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions. IEEE Trans. Geosci. Remote Sens. 43(3), 455–465 (2005)CrossRefGoogle Scholar
  25. 25.
    Yokoya, N., Grohnfeldt, C., Chanussot, J.: Hyperspectral and multispectral data fusion: a comparative review of the recent literature. IEEE Geosci. Remote Sens. Mag. 5(2), 29–56 (2017)CrossRefGoogle Scholar
  26. 26.
    Ghasrodashti, E.K., Karami, A., Heylen, R., Scheunders, P.: Spatial resolution enhancement of hyperspectral images using spectral unmixing and Bayesian sparse representation. Remote Sens. 9(6), 541 (2017)CrossRefGoogle Scholar
  27. 27.
    Sun, X., Zhang, L., Yang, H., Wu, T., Cen, Y., Guo, Y.: Enhancement of spectral resolution for remotely sensed multispectral image. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2198–2211 (2015)CrossRefGoogle Scholar
  28. 28.
    Zhang, Y.: Spatial resolution enhancement of hyperspectral image based on the combination of spectral mixing model and observation model. In: Image and Signal Processing for Remote Sensing XX, vol. 9244, p. 924405. International Society for Optics and Photonics (2014)Google Scholar
  29. 29.
    Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G.A., Restaino, R., Wald, L.: A critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens. 53(5), 2565–2586 (2015)CrossRefGoogle Scholar
  30. 30.
    Loncan, L., de Almeida, L.B., Bioucas-Dias, J.M., Briottet, X., Chanussot, J., Dobigeon, N., Fabre, S., Liao, W., Licciardi, G.A., Simoes, M.: Hyperspectral pansharpening: a review. IEEE Geosci. Remote Sens. Mag. 3(3), 27–46 (2015)CrossRefGoogle Scholar
  31. 31.
    Amolins, K., Zhang, Y., Dare, P.: Wavelet based image fusion techniques: an introduction, review and comparison. ISPRS J. Photogramm. Remote Sens. 62(4), 249–263 (2007)CrossRefGoogle Scholar
  32. 32.
    Fechner, T., Godlewski, G.: Optimal fusion of TV and infrared images using artificial neural networks. In: Applications and Science of Artificial Neural Networks, vol. 2492, pp. 919–926. International Society for Optics and Photonics (1995)Google Scholar
  33. 33.
    Gross, H.N., Schott, J.R.: Application of spectral mixture analysis and image fusion techniques for image sharpening. Remote Sens. Environ. 63(2), 85–94 (1998)CrossRefGoogle Scholar
  34. 34.
    Khan, M.M., Chanussot, J., Alparone, L.: Pansharpening of hyperspectral images using spatial distortion optimization. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 2853–2856. IEEE (2009)Google Scholar
  35. 35.
    Mianji, F.A., Zhang, Y., Gu, Y., Babakhani, A.: Spatial-spectral data fusion for resolution enhancement of hyperspectral imagery. In: IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), vol. 3, pp. III–1011. IEEE (2009)Google Scholar
  36. 36.
    Peng, H., Rao, R.: Hyperspectral image enhancement with vector bilateral filtering. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 3713–3716. IEEE (2009)Google Scholar
  37. 37.
    Karoui, M.S., Deville, Y., Benhalouche, F.Z., Boukerch, I.: Hypersharpening by joint-criterion nonnegative matrix factorization. IEEE Trans. Geosci. Remote Sens. 55(3), 1660–1670 (2017)CrossRefGoogle Scholar
  38. 38.
    Qu, J., Li, Y., Dong, W.: Guided filter and principal component analysis hybrid method for hyperspectral pansharpening. J. Appl. Remote Sens. 12(1), 015003 (2018)CrossRefGoogle Scholar
  39. 39.
    Vivone, G., Restaino, R., Chanussot, J.: A regression-based high-pass modulation pansharpening approach. IEEE Trans. Geosci. Remote Sens. 56(2), 984–996 (2018)zbMATHCrossRefGoogle Scholar
  40. 40.
    Wang, M., Zhang, K., Pan, X., Yang, S.: Sparse tensor neighbor embedding based pan-sharpening via N-way block pursuit. Knowl.-Based Syst. 149, 18–33 (2018)CrossRefGoogle Scholar
  41. 41.
    Yuan, Q., Wei, Y., Meng, X., Shen, H., Zhang, L.: A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(3), 978–989 (2018)CrossRefGoogle Scholar
  42. 42.
    Yang, J., Zhao, Y.Q., Chan, J.C.W.: Hyperspectral and multispectral image fusion via deep two-branches convolutional neural network. Remote Sens. 10(5), 800 (2018)CrossRefGoogle Scholar
  43. 43.
    Xing, Y., Wang, M., Yang, S., Jiao, L.: Pan-sharpening via deep metric learning. ISPRS J. Photogramm. Remote Sens. (2018)Google Scholar
  44. 44.
    Chen, G., Qian, S.E.: Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 49(3), 973–980 (2011)CrossRefGoogle Scholar
  45. 45.
    Rasti, B., Sveinsson, J.R., Ulfarsson, M.O.: Wavelet-based sparse reduced-rank regression for hyperspectral image restoration. IEEE Trans. Geosci. Remote Sens. 52(10), 6688–6698 (2014)CrossRefGoogle Scholar
  46. 46.
    Zelinski, A., Goyal, V.: Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation. In: IEEE International Conference on Geoscience and Remote Sensing Symposium, pp. 387–390. IEEE (2006)Google Scholar
  47. 47.
    Yuan, Q., Zhang, L., Shen, H.: Hyperspectral image denoising employing a spectral–spatial adaptive total variation model. IEEE Trans. Geosc. Remote Sens. 50(10), 3660–3677 (2012)CrossRefGoogle Scholar
  48. 48.
    Santhosh, S., Abinaya, N., Rashmi, G., Sowmya, V., Soman, K.: A novel approach for denoising coloured remote sensing image using Legendre Fenchel transformation. In: International Conference on Recent Trends in Information Technology (ICRTIT), pp. 1–6. IEEE (2014)Google Scholar
  49. 49.
    Reshma, R., Sowmya, V., Soman, K.: Effect of Legendre-Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification. Neural Comput. Appl. 29(8), 301–310 (2018)CrossRefGoogle Scholar
  50. 50.
    Srivatsa, S., Ajay, A., Chandni, C., Sowmya, V., Soman, K.: Application of least square denoising to improve ADMM based hyperspectral image classification. Procedia Comput. Sci. 93, 416–423 (2016)CrossRefGoogle Scholar
  51. 51.
    Zhong, P., Wang, R.: Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 51(4), 2260–2275 (2013)CrossRefGoogle Scholar
  52. 52.
    Li, Q., Li, H., Lu, Z., Lu, Q., Li, W.: Denoising of hyperspectral images employing two-phase matrix decomposition. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(9), 3742–3754 (2014)CrossRefGoogle Scholar
  53. 53.
    He, W., Zhang, H., Zhang, L., Shen, H.: Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration. IEEE Trans. Geosci. Remote Sens. 54(1), 178–188 (2016)CrossRefGoogle Scholar
  54. 54.
    Ma, J., Li, C., Ma, Y., Wang, Z.: Hyperspectral image denoising based on low-rank representation and superpixel segmentation. In: IEEE International Conference on Image Processing (ICIP), pp. 3086–3090. IEEE (2016)Google Scholar
  55. 55.
    Bai, X., Xu, F., Zhou, L., Xing, Y., Bai, L., Zhou, J.: Nonlocal similarity based nonnegative tucker decomposition for hyperspectral image denoising. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(3), 701–712 (2018)CrossRefGoogle Scholar
  56. 56.
    Zhuang, L., Bioucas-Dias, J.M.: Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(3), 730–742 (2018)CrossRefGoogle Scholar
  57. 57.
    Camps-Valls, G., Bruzzone, L.: Kernel Methods for Remote Sensing Data Analysis. Wiley Online Library (2009)Google Scholar
  58. 58.
    Ang, J.C., Mirzal, A., Haron, H., Hamed, H.: Supervised, unsupervised and semi-supervised feature selection: A review on gene selection. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(5), 971–989 (2016)CrossRefGoogle Scholar
  59. 59.
    Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci. Remote Sens. Lett. 10(2), 318–322 (2013)CrossRefGoogle Scholar
  60. 60.
    Foody, G.M., Mathur, A.: A relative evaluation of multiclass image classification by support vector machines. IEEE Trans. Geosci. Remote Sens. 42(6), 1335–1343 (2004)CrossRefGoogle Scholar
  61. 61.
    Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., Plaza, A.: Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art. IEEE Geosci. Remote Sens. Mag. 5(4), 37–78 (2017)CrossRefGoogle Scholar
  62. 62.
    Wang, M., Wan, Y., Ye, Z., Lai, X.: Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Inf. Sci. 402, 50–68 (2017)CrossRefGoogle Scholar
  63. 63.
    Chen, Y., Nasrabadi, N.M., Tran, T.D.: Sparse representation for target detection in hyperspectral imagery. IEEE J. Sel. Top. Signal Process. 5(3), 629–640 (2011)CrossRefGoogle Scholar
  64. 64.
    Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005)CrossRefGoogle Scholar
  65. 65.
    Li, J., Bioucas-Dias, Jose, M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geosci. Remote Sens. 48(11), 4085–4098 (2010)Google Scholar
  66. 66.
    Cai, T.T., Wang, L.: Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans. Inf. Theory 57(7), 4680–4688 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  67. 67.
    Davenport, M.A., Wakin, M.B.: Analysis of orthogonal matching pursuit using the restricted isometry property. IEEE Trans. Inf. Theory 56(9), 4395–4401 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  68. 68.
    Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  69. 69.
    Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973–3985 (2011)CrossRefGoogle Scholar
  70. 70.
    Nikhila, H., Sowmya, V., Soman, K.: Gurls vs libsvm: performance comparison of kernel methods for hyperspectral image classification. Indian J. Sci. Technol. 8(24), 1–10 (2015)Google Scholar
  71. 71.
    Tacchetti, A., Mallapragada, P.S., Santoro, M., Rosasco, L.: GURLS: A Toolbox for Regularized Least Squares Learning (2012)Google Scholar
  72. 72.
    Soman, K., Loganathan, R., Ajay, V.: Machine Learning with SVM and Other Kernel Methods. PHI Learning Pvt. Ltd. (2009)Google Scholar
  73. 73.
    Soman, K., Diwakar, S., Ajay, V.: Data Mining: Theory and Practice. PHI Learning Pvt. Ltd. (2006)Google Scholar
  74. 74.
    Gualtieri, J., Chettri, S.R., Cromp, R., Johnson, L.: Support vector machine classifiers as applied to AVIRIS data. In: Proceedings of Eighth JPL Airborne Geoscience Workshop (1999)Google Scholar
  75. 75.
    Steinwart, I., Christmann, A.: Support Vector Machines. Springer Science & Business Media (2008)Google Scholar
  76. 76.
    Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)CrossRefGoogle Scholar
  77. 77.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)CrossRefGoogle Scholar
  78. 78.
    Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., van de Walle, R.: Hyperspectral image classification with convolutional neural networks. The 23rd ACM International Conference on Multimedia, pp. 1159–1162 (2015)Google Scholar
  79. 79.
    Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.: Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43(3), 492–501 (2005)CrossRefGoogle Scholar
  80. 80.
    Rajan, S., Ghosh, J., Crawford, M.M.: Exploiting class hierarchies for knowledge transfer in hyperspectral data. IEEE Trans. Geosci. Remote Sens. 44(11), 3408–3417 (2006)CrossRefGoogle Scholar
  81. 81.
    Jun, G., Ghosh, J.: Spatially adaptive semi-supervised learning with Gaussian processes for hyperspectral data analysis. Stat. Anal. Data Min. 4(4), 358–371 (2011)MathSciNetCrossRefGoogle Scholar
  82. 82.
    Dópido, I., Li, J., Marpu, P.R., Plaza, A., Bioucas Dias, J.M., Benediktsson, J.A.: Semisupervised self-learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 51(7), 4032–4044 (2013)CrossRefGoogle Scholar
  83. 83.
    Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.: Spectral and spatial classification of hyperspectral data using svms and morphological profiles. IEEE Trans. Geosci. Remote Sens. 46(11), 3804–3814 (2008)CrossRefGoogle Scholar
  84. 84.
    Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geosci. Remote Sens. 48(11), 4085–4098 (2010)Google Scholar

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Authors and Affiliations

  1. 1.Amrita School of EngineeringCenter for Computational Engineering and Networking (CEN), Amrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.Faculty of Computers and Information, Computer Science DepartmentSouth Valley UniversityLuxorEgypt

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