Skip to main content
SpringerLink
Log in

Spectral-Spatial Methods for Hyperspectral Image Classification. Review

  • Analysis and Synthesis of Signals and Images
  • Published:
Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

Various methods of spectral-spatial classification of hyperspectral data are reviewed. Papers devoted to the most popular ways of using spatial information for increasing the accuracy of classification maps are considered. It is shown that the best results are obtained by using preprocessing of “raw” data before the procedures of pixel-wise spectral classification. Disadvantages, limits, and possible directions for developing existing methods are investigated and analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Promising Information Technologies for Earth’s Remote Sensing, Ed. by V. A. Soifer (Novaya Tekhnika, Samara, 2015) [in Russian].

  2. V. G. Bondur, “Modern Approaches to Processing Large Hyperspectral and Multispectral Aerospace Data Flows,” Issledovanie Zemli Iz Kosmosa, No. 1, 4–16 (2014) [Izvestiya, Atmospheric and Oceanic Physics 50 (9), 840–852 (2014)].

    Google Scholar 

  3. I. S. Gruzman, V. S. Kirichuk, V. P. Kosykh, et al., Digital Image Processing in Information Systems (Izd. Nov. Gos. Tekh. Univ., Novosibirsk, 2002) [in Russian].

    Google Scholar 

  4. A. A. Buchnev and V. P. Pyatkin, “Classification of Hyperspectral Data of Earths Remote Sensing with Learning,” in Proceedings of the XIII International Scientific Congress “Interekspo Geo-Sibir’-2017” (Siberian State University of Geosystems and Technologies, Novosibirsk, 2017), Vol. 4, pp. 8–12 [in Russian].

    Google Scholar 

  5. A. Plaza, Q. Du, J. M. Bioucas-Dias, et al., “Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data,” IEEE Trans. Geosci. Remote Sensing 49 (11), 4103–4110 (2011).

    Article  Google Scholar 

  6. V. V. Kozoderov, T. V. Kondranin, and E. V. Dmitriev, “Recognition of Natural and Man-Made Objects in Airborne Hyperspectral Images,” Issledovanie Zemli Iz Kosmosa, No. 1, 35–42 (2014) [Izvestiya, Atmospheric and Oceanic Physics 50 (9), 878–886 (2014)].

    Google Scholar 

  7. V. V. Asmus, A. A. Buchnev, and V. P. Pyatkin “Controlled Classification of Earth Remote Sensing Data,” Avtometriya 44 (4), 60–67 (2008) [Optoelectron., Instrum. Data Process. 44 (4), 331–336 (2008)].

    Google Scholar 

  8. R. L. Kettig and D. A. Landgrebe, “Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects,” IEEE Trans. Geosci. Electron. GE-14 (1), 19–26 (1976).

    Article  ADS  Google Scholar 

  9. D. Scholz, J. Russell, J. Lindenlaub, and P. Swain, “A Case Study Using ECHO (Extraction and Classification of Homogeneous Objects) for Analysis of Multispectral Scanner Data,” LARS Techn. Rep. 105, 86 (1977).

    Google Scholar 

  10. U. C. Benz, P. Hofmann, G. Willhauck, et al., “Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information,” ISPRS J. Photogram. Remote Sensing 58 (3–4), 239–258 (2004).

    Article  ADS  Google Scholar 

  11. S. Ryherd and C. E. Woodcock, “Combining Spectral and Texture Data in the Segmentation of Remotely Sensed Images,” Photogram. Eng. Remote Sensing 62 (2), 181–194 (1996).

    Google Scholar 

  12. L.-Z. Huo and P. Tang, “Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Gabor Textures,” in Proc. IEEE Intern. Geosci. and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24–29, 2011, pp. 1708–1711.

    Google Scholar 

  13. L. Zhang, L. Zhang, D. Tao, and X. Huang, “On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification,” IEEE Trans. Geosci. Remote Sensing 50, 879–893 (2012).

    Article  ADS  Google Scholar 

  14. C. C. Chang and C. J. Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Trans. Intel. Syst. Technol. 2 (3), 1–27 (2011).

    Article  Google Scholar 

  15. C. Chen, W. Li, H. Su, and K. Liu, “Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine,” Remote Sensing 6 (6), 5795–5814 (2014). DOI: 10.3390/rs6065795.

    Article  ADS  Google Scholar 

  16. A. Plaza, P. Martinez, R. Perez, and J. Plaza, “A New Approach to Mixed Pixel Classification of Hyperspectral Imagery Based on Extended Morphological Profiles,” Pattern Recogn. 37 (6), 1097–1116 (2004).

    Article  Google Scholar 

  17. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of Hyperspectral Data from Urban Areas Based on Extended Morphological Profiles,” IEEE Trans. Geosci. Remote Sensing 43 (3), 480–491 (2005).

    Article  ADS  Google Scholar 

  18. P. Ghamisi, M. Dalla Mura, and J. A. Benediktsson, “A Survey on Spectral-Spatial Classification Techniques Based on Attribute Profiles,” IEEE Trans. Geosci. Remote Sensing 53 (5), 2335–2353 (2015).

    Article  ADS  Google Scholar 

  19. M. Fauvel, J. Chanussot, J. A. Benediktsson, and J. R. Sveinsson, “Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles,” IEEE Trans. Geosci. Remote Sensing 46 (11), 3804–3814 (2008).

    Article  ADS  Google Scholar 

  20. D. Tuia, M. Volpi, M. Dalla Mura, et al., “Automatic Feature Learning for Spatio-Spectral Image Classification with Sparse SVM,” IEEE Trans. Geosci. Remote Sensing. 52 (10), 6062–6074 (2014).

    Article  ADS  Google Scholar 

  21. M. Dalla Mura, A. Villa, J. A. Benediktsson, et al., “Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis,” IEEE Geosci. Remote Sensing Lett. 8 (3), 542–546 (2011).

    Article  ADS  Google Scholar 

  22. E. T. Gormus, N. Canagarajah, and A. Achim, “Dimensionality Reduction of Hyperspectral Images with Wavelet Based Empirical Mode Decomposition,” in Proc. of the 18th IEEE Intern. Conf. on Image Processing, Brussels, Belgium, September 11–14, 2011, pp. 1709–1712.

    Google Scholar 

  23. E. S. Nezhevenko, A. S. Feoktistov, and O. Yu. Dashevskii, “Neural Network Classification of Hyperspectral Images on the Basis of the Hilbert–Huang Transform,” Avtometriya 53 (2), 79–85 (2017) [Optoelectron., Instrum. Data Process. 53 (2), 165–170 (2017)].

    Google Scholar 

  24. T. M. Lillesand, R. W. Kiefer, and J. W. Chipman, Remote Sensing and Image Interpretation (John Wiley & Sons, New York, 2004).

    Google Scholar 

  25. D. P. Hader, Image Analysis: Methods and Applications (CRC Press, London, 2000).

    Book  Google Scholar 

  26. Y. Qian, M. Ye, and J. Zhou, “Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features,” IEEE Trans. Geosci. Remote Sensing 51 (4), 2276–2291 (2013).

    Article  ADS  Google Scholar 

  27. T. C. Bau, S. Sarkar, and G. Healey, “Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank,” IEEE Trans. Geosci. Remote Sensing 48 (9), 3457–3464 (2010).

    Article  ADS  Google Scholar 

  28. L. Shen and S. Jia, “Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification,” IEEE Trans. Geosci. Remote Sensing 49 (12), 5039–5046 (2011).

    Article  ADS  Google Scholar 

  29. Y. Tang, Y. Lu, and H. Yuan, “Hyperspectral Image Classification Based on Three-Dimensional Scattering Wavelet Transform,” IEEE Trans. Geosci. Remote Sensing 53 (5), 2467–2480 (2015).

    Article  ADS  Google Scholar 

  30. C. Chen, W. Li, E. W. Tramel, et al., “Spectral-Spatial Preprocessing Using Multihypothesis Prediction for Noise-Robust Hyperspectral Image Classification,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing 7 (4), 1047–1059 (2014).

    Article  ADS  Google Scholar 

  31. F. Palsson, M. Ulfarsson, and J. R. Sveinsson, “Hyperspectral Image Denoising Using a Sparse Low Rank Model and Dual-Tree Complex Wavelet Transform,” in Proc. IEEE Intern. Conf. on Geosci. and Remote Sensing, Quebec, Canada, 13–18 July, 2014.

    Google Scholar 

  32. Y. Zhen, H. Mingyi, J. E. Fowler, and D. Qian, “Hyperspectral Image Classification Based on Spectra Derivative Features and Locality Preserving Analysis,” in Proc. of the Signal and Information Processing (ChinaSIP), IEEE China Summit & Intern. Conf. Xi’an, China, 9–13 July, 2014.

    Google Scholar 

  33. A. N. Tikhonov and V. Ya. Arsenin, Solution of Ill-Posed Problems (Nauka, Moscow, 1979; Winston & Sons, Washington, 1977).

    MATH  Google Scholar 

  34. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge University Press, Cambridge, 2000).

    Book  MATH  Google Scholar 

  35. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme Learning Machine: Theory and Applications,” Neurocomputing 70, 489–501 (2006).

    Article  Google Scholar 

  36. G.-B. Huang, L. Chen, and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes,” IEEE Trans. Neural Networks 17 (4), 879–892 (2006).

    Article  Google Scholar 

  37. N. E. Huang, Z. Shen, S. R. Long, et al., “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis,” in Proc. Royal Soc. London. Ser. A 454 (1971), 903–995 (1998).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  38. M. F. Baumgardner, L. L. Biehl, and D. A. Landgrebe, “220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992. Indian Pine Test Site 3,” Purdue University Research Repository, 2015. DOI: 10.4231/R7RX991C.

    Google Scholar 

  39. M. Pesaresi and J. A. Benediktsson, “A New Approach for the Morphological Segmentation of High-Resolution Satellite Imagery,” IEEE Trans. Geosci. Remote Sensing 39 (2), 309–320 (2001).

    Article  ADS  Google Scholar 

  40. P. Soille, Morphological Image Analysis, Principles and Applications (Springer Verlag, Berlin, 2003).

  41. C. Zhu, W. Shi, M. Pesaresi, et al., “The Recognition of Road Network from High-Resolution Satellite Remotely Sensed Data Using Image Morphological Characteristics,” Intern. J. Remote Sensing 26 (24), 5493–5508 (2005).

    Article  ADS  Google Scholar 

  42. E. J. Breen and R. Jones, “Attribute Openings, Thinnings, and Granulometries,” Computer Visual and Image Understanding 64 (3), 377–389 (1996).

    Article  Google Scholar 

  43. M. Dalla Mura, J. A. Benediktsson, B.Waske, and L. Bruzzone, “Morphological Attribute Filters for the Analysis of Very High Resolution Images,” in Proc. IEEE Intern. Geosci. and Remote Sensing Symposium (IGARSS 2009), Cape Town, South Africa, 13–17 July, 2009.

    Google Scholar 

  44. M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, “Morphological Attribute Profiles for the Analysis of Very High Resolution Images,” IEEE Trans. Geosci. Remote Sensing 48 (10), 3747–3762 (2010).

    Article  ADS  Google Scholar 

  45. S. R. Joelsson, J. A. Benediktsson, and J. R. Sveinsson, “Random Forest Classifiers for Hyperspectral Data,” in Proc. IEEE Intern. Geosci. and Remote Sensing Symposium (IGARSS 2005), Seoul, Korea, 25–29 July, 2005.

    Google Scholar 

  46. M. Dalla Mura, J. A. Benediktsson, B.Waske, and L. Bruzzone, “Extended Profiles with Morphological Attribute Filters for the Analysis of Hyperspectral Data,” Intern. J. Remote Sensing 31 (22), 5975–5991 (2010).

    Article  ADS  Google Scholar 

  47. E. Aptoula, “Hyperspectral Image Classification with Multidimensional Attribute Profiles,” IEEE Geosci. Remote Sensing Lett. 12 (10), 2031–2035 (2015).

    Article  ADS  Google Scholar 

  48. M.-T. Pham, E. Aptoula, and S. Lefèvre, “Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing 11 (1), 249–256 (2018). DOI: 10.1109/JSTARS.2017.2773367.

    Article  ADS  Google Scholar 

  49. B. Demir and L. Bruzzone, “Histogram-Based Attribute Profiles for Classification of Very High Resolution Remote Sensing Images,” IEEE Trans. Geosci. Remote Sensing 54 (4), 2096–2107 (2016).

    Article  ADS  Google Scholar 

  50. R. Battiti, B. Demir, and L. Bruzzone, “Compressed Histogram Attribute Profiles for the Classification of VHR Remote Sensing Images,” Proc. SPIE 9643, 96430R (2015).

    Article  ADS  Google Scholar 

  51. M.-T. Pham, S. Lefèvre, and E. Aptoula, “Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification,” IEEE Trans. Geosci. Remote Sensing 56 (2), 1199–1212 (2018).

    Article  ADS  Google Scholar 

  52. M.-T. Pham, S. Lefèvre, E. Aptoula, and B. B. Damodaran, “Classification of VHR Remote Sensing Images Using Local Feature-Based Attribute Profiles,” in Proc. IEEE Intern. Geosci. and Remote Sensing Symposium (IGARSS 2011), Texas, USA, 23–28 July, 2017.

    Google Scholar 

  53. E. Aptoula, M. C. Ozdemir, and B. Yanikoglu, “Deep Learning with Attribute Profiles for Hyperspectral Image Classification,” IEEE Geosci. Remote Sensing Lett. 13 (12), 1970–1974 (2016).

    Article  ADS  Google Scholar 

  54. B. Sun, X. Kang, S. Li, and J. A. Benediktsson, “Random-Walker-Based Collaborative Learning for Hyperspectral Image Classification,” IEEE Trans. Geosci. Remote Sensing 55 (1), 212–222 (2017).

    Article  ADS  Google Scholar 

  55. B. Sun, X. Kang, S. Li, et al., “Recent Developments from Attribute Profiles for Remote Sensing Image Classification,” in Proc. of the Intern. Conf. on Pattern Recognition and Artificial Intelligence, Montreal, Canada, May 14–17, 2018. https://arxiv.org/pdf/1803.10036.pdf.

    Google Scholar 

  56. E. A. Zimichev, N. L. Kazanskiy, and P. G. Serafimovich, “Spectral-Spatial Classification with K-MEANS++ Partitional Clustering,” Komp’yuternaya Optika 38 (2), 281–286 (2014) [Proc. SPIE. Optical Technologies for Telecommunications, 2014. 95330M (2015)].

    ADS  Google Scholar 

  57. S. M. Borzov, P. V. Mel’nikov, I. A. Pestunov, et al., “Complex Processing of Hyperspectral Images on the Basis of Spectral and Spatial Information,” Vych. Tekhnol. 21 (1), 25–39 (2016).

    Google Scholar 

  58. M. Borhani and H. Ghassemian, “Hyperspectral Image Classification Based on Spectral-Spatial Features Using Probabilistic SVM and Locally Weighted Markov Random Fields,” in Proc. of the Iranian Conf. on Intelligent Systems (ICIS 2014), Bam, Iran, February 4–6, 2014.

    Google Scholar 

  59. Y. Hu, E. Saber, S. T. Monteiro, et al., “Classification of Hyperspectral Images Based on Conditional Random Fields,” Proc. SPIE. 2015. 9405. 940510. http://dx.doi.org/10.1117/12.2083374.

  60. Y. Tarabalka and A. Rana, “Graph-Cut-Based Model for Spectral-Spatial Classification of Hyperspectral Images,” in Proc. IEEE Intern. Geosci. and Remote Sensing Symposium (IGARSS 2014), Quebec, Canada, July 13–18, 2014, pp. 3418–3421.

    Google Scholar 

  61. F. A. Kruse, A. B. Lefkoff, J. W. Boardman, et al., “The Spectral Image Processing System (SIPS) — Interactive Visualization and Analysis of Imaging Spectrometer Data,” Remote Sensing of Environment 44 (2–3), 145–163 (1993).

    Article  ADS  Google Scholar 

  62. H. Du, C. Chang, H. Ren, et al., “New Hyperspectral Discrimination Measure for Spectral Characterization,” Opt. Eng. 2004. 43 (8), 1777–1786 (2004).

    Google Scholar 

  63. Y. Boykov, O. Veksler, and R. Zabih, “Fast Approximate Energy Minimization Via Graph Cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23 (11), 1222–1239 (2001).

    Article  Google Scholar 

  64. K. Huang, S. Li, X. Kang, and L. Fang, “Spectral-Spatial Hyperspectral Image Classification Based on KNN,” Sens. Imaging 17 (1) (2016). https://doi.org/10.1007/s11220-015-0126-z.

    Google Scholar 

  65. W. Song, S. Li, X. Kang, and K. Huang, “Hyperspectral Image Classification Based on KNN Sparse Representation,” in Proc. of IEEE Intern. Geosci. and Remote Sensing Symposium (IGARSS 2016), Beijing, China, July 10–15, 2016, pp. 2411–2414.

    Google Scholar 

  66. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and Classification of Hyperspectral Images Using Minimum Spanning Forest Grown from Automatically Selected Markers,” IEEE Trans. Syst., Man Cybern. Pt. B: Cybernetics. 40 (5), 1267–1279 (2010).

    Article  MATH  Google Scholar 

  67. J.-F. Stawiaski, Mathematical Morphology and Graphs: Application to Interactive Medical Image Segmentation. Ph. D. Thesis (Paris School of Mines, Paris, France, 2008).

    Google Scholar 

  68. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 1990).

    MATH  Google Scholar 

  69. D. Akbari, “A New Spectral-Spatial Framework for Classification of Hyperspectral Data,” in Intern. Arch. Photogramm. Remote Sensing Spatial Inform. Sci. 2017. Vol. XLII-4/W5, pp. 7–10. https://doi.org/10.5194/isprsarchives-XLII-4-W5-7-2017.

    Google Scholar 

  70. V. B. Berikov, I. A. Pestunov, N. M. Karaev, and A. Tevari, “Recognition of Hyperspectral Images Using a Cluster Ensemble and Partially Controlled Learning,” in Proceedings of the International Conference “Spatial Data Processing in the Problems of Monitoring of Natural and Man-Made Processes (SDM-2017) (Institute of Computational Technologies, Novosibirsk, 2017). http://conf.nsc.ru/files/conferences/SDM-2017/427493/ (SDM-2017)/Thesis.pdf.

    Google Scholar 

  71. V. Berikov and I. Pestunov, “Ensemble Clustering Based on Weighted Co-Association Matrices: Error Bound and Convergence Properties,” Pattern Recogn. 63 (C), 427–436 (2017).

    Article  Google Scholar 

  72. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis (Cambridge University Press, New York, 2004).

    Book  MATH  Google Scholar 

  73. M. AbdelFattah, L. F. AbdelAal, and R. El-khoribi, “Spectral-Spatial Hyperspectral Image Classification Based on Randomized Singular Value Decomposition and 3-Dimensional Discrete Wavelet Transform,” Intern. J. Comput. Appl. 180 (30), 1–10 (2018). 0975–8887.

    Google Scholar 

  74. X. Cao, L. Xu, D. Meng, et al., “Integration of 3-Dimensional Discrete Wavelet Transform and Markov Random Field for Hyperspectral Image Classification,” Neurocomputing 226, 90–100 (2017). http://dx.doi.org/10.1016/ j.neucom.2016.11.034.

    Article  Google Scholar 

  75. I. Goodfellow, Yo. Bengio, and A. Courville, Deep Learning (MIT Press, 2016).

    MATH  Google Scholar 

  76. S. Nikolenko, A. Kadurin, and E. Arkhangelskaya, Deep Learning. Immersion in the World of Neural Networks (Piter, Saint-Petersburg, 2018) [in Russian].

    Google Scholar 

  77. L. Zhang, L. Zhang, and B. Du, “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art,” IEEE Geosci. Remote Sensing Mag. 4 (2), 22–40 (2016).

    Article  Google Scholar 

  78. Y. Chen, Z. Lin, X. Zhao, and G. Wang, “Deep Learning-Based Classification of Hyperspectral Data,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing 7 (6), 2094–2107 (2014).

    Article  ADS  Google Scholar 

  79. T. Li, J. Zhang, and Y. Zhang, “Classification of Hyperspectral Image Based on Deep Belief Networks,” in Proc. of the IEEE Intern. Conf. on Image Processing, Paris, France, 27–30 Oct., 2014.

    Google Scholar 

  80. Y. Chen, X. Zhao, and X. Jia, “Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing 8 (6), 2381–2392 (2015).

    Article  ADS  Google Scholar 

  81. W. Zhao and S. Du, “Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach,” IEEE Trans. Geosci. Remote Sensing 54 (8), 4544–4554 (2016).

    Article  ADS  Google Scholar 

  82. J. Yue, W. Zhao, S. Mao, and H. Liu, “Spectral-Spatial Classification of Hyperspectral Images Using Deep Convolutional Neural Networks,” Remote Sensing Lett. 6 (6), 468–477 (2015).

    Article  Google Scholar 

  83. Y. Li, H. Zhang, and Q. Shen, “Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network,” Remote Sensing 9 (1), 67 (2017). DOI: 10.3390/rs9010067.

    Article  ADS  Google Scholar 

  84. K. Makantasis, K. Karantzalos, A. Doulamis, and N. Doulamis, “Deep Supervised Learning for Hyperspectral Data Classification Through Convolutional Neural Networks,” in Proc. of the IEEE Intern. Geosci. and Remote Sensing Symposium (IGARSS 2015), Milan, Italy, July 26–31, 2015, pp. 4959–4962.

    Google Scholar 

  85. W. Wei, J. Zhang, L. Zhang, et al., “Deep Cube-Pair Network for Hyperspectral Imagery Classification,” Remote Sensing 10 (5), 783 (2018).

    Article  ADS  Google Scholar 

  86. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in Proc. of the Intern. Conf. on Learning Representations, San Diego, USA, 7–9 May, 2015. https://arxiv.org/ pdf/1409.1556.

    Google Scholar 

  87. S. M. Borzov, A. O. Potaturkin, O. I. Potaturkin, and A. M. Fedotov, “Analysis of the Efficiency of Classification of Hyperspectral Satellite Images of Natural and Man-Made Areas,” Avtometriya 52 (1), 3–14 (2016) [Optoelectron., Instrum. Data Process. 52 (1), 1–10 (2016)].

    Google Scholar 

  88. S. M. Borzov and O. I. Potaturkin, “Efficiency of the Spectral-Spatial Classification of Hyperspectral Imaging Data,” Avtometriya 53 (1), 32–42 (2017) [Optoelectron., Instrum. Data Process. 53 (1), 26–34 (2017)].

    Google Scholar 

  89. P. V. Melnikov, I. A. Pestunov, S. A. Rylov, “Comparison of Spectral-Spatial Classification Methods for Hyperspectral Images of High Spatial Resolution,” J. Siberian Federal University. Engng Technol. 10 (6), 805–811 (2017).

    Article  Google Scholar 

  90. P. V. Mel’nikov, I. A. Pestunov, and S. A. Rylov, “Experimental “Comparison of Classification Methods of Hyperspectral Images of High Spatial Resolution by Spectral and Spatial Features,” in Proc. of the III International Scientific Conference “Regional Problems of Remote Sensing of the Earth” (Siberian Federal University, Krasnoyarsk, 2016), pp. 28–33.

    Google Scholar 

  91. M. A. Gur’yanov and S. M. Borzov, “Spectral-Spatial Classification of Vegetation by Hyperspectral Data,” Vestn. Nov. Gos. Univ. Informatsionnye Tekhnologii 15 (4), 14–21 (2017).

    Google Scholar 

  92. S. M. Borzov and O. I. Potaturkin, “Classification of Hyperspectral Images with Different Methods of Training Set Formation,” Avtometriya 54 (1), 89–97 (2018) [Optoelectron., Instrum. Data Process. 54 (1), 76–82 (2018)].

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, pr. Akademika Koptyuga 1, Novosibirsk, 630090, Russia

    S. M. Borzov & O. I. Potaturkin

Authors
  1. S. M. Borzov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. O. I. Potaturkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. M. Borzov.

Additional information

Original Russian Text © S.M. Borzov, O.I. Potaturkin, 2018, published in Avtometriya, 2018, Vol. 54, No. 6, pp. 64–86.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borzov, S.M., Potaturkin, O.I. Spectral-Spatial Methods for Hyperspectral Image Classification. Review. Optoelectron.Instrument.Proc. 54, 582–599 (2018). https://doi.org/10.3103/S8756699018060079

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S8756699018060079

Keywords

Search

Navigation