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Spectral-spatial Classification of Hyperspectral Images Using Signal Subspace Identification and Edge-preserving Filter

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Abstract

Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects. In this paper, a new method of classifying hyperspectral images using spectral spatial information has been presented. Here, using the hyperspectral signal subspace identification (HYSIME) method which estimates the signal and noise correlation matrix and selects a subset of eigenvalues for the best representation of the signal subspace in order to minimize the mean square error, subsets from the main sample space have been extracted. After subspace extraction with the help of the HYSIME method, the edge-preserving filtering (EPF), and classification of the hyperspectral subspace using a support vector machine (SVM), results were then merged into the decision-making level using majority rule to create the spectral-spatial classifier. The simulation results showed that the spectral-spatial classifier presented leads to significant improvement in the accuracy and validity of the classification of Indiana, Pavia and Salinas hyperspectral images, such that it can classify these images with 98.79%, 98.88% and 97.31% accuracy, respectively.

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References

  1. H. Liu, G. F. Xiao, Y. L. Tan, C. J. Ouyang. Multi-source remote sensing image registration based on contourlet transform and multiple feature fusion. International Journal of Automation and Computing, to be published. DOI: https://doi.org/10.1007/s11633-018-1163-6.

  2. D. Chutia, D. K. Bhattacharyya, K. K. Sarma, R. Kalita, S. Sudhakar. Hyperspectral remote sensing classifications: A perspective survey. Transactions in GIS, vol. 20, no. 4, pp. 463–490, 2016. DOI: https://doi.org/10.1111/tgis.12164.

    Article  Google Scholar 

  3. R. K. Pina, R. C. Puetter. Bayesian image reconstruction: The pixon and optimal image modeling. Publications of the Astronomical Society of the Pacific, vol. 105, no. 688, pp. 630–637, 1993. DOI: 10.1086/133207.

    Article  Google Scholar 

  4. L. Velásquez, J. P. Cruz-Tirado, R. Siche, R. Quevedo. An application based on the decision tree to classify the marbling of beef by hyperspectral imaging. Meat Science, vol. 133, pp. 43–50, 2017. DOI: https://doi.org/10.1016/j.meatsci.2017.06.002.

    Article  Google Scholar 

  5. E. Merényi, W. H. Farrand, J. V. Taranik, T. B. Minor. Classification of hyperspectral imagery with neural networks: Comparison to conventional tools. EURASIP Journal on Advances in Signal Processing, vol. 2014, no. 1, Article number 71, 2014. DOI: https://doi.org/10.1186/1687-6180-2014-71.

    Article  Google Scholar 

  6. M. Fauvel, J. Chanussot, J. A. Benediktsson. A spatial-spectral kernel-based approach for the classification of remote-sensing images. Pattern Recognition, vol. 45, no. 1, pp. 381–392, 2012. DOI: https://doi.org/10.1016/j.patcog.2011.03.035.

    Article  Google Scholar 

  7. G. Mountrakis, J. Im, C. Ogole. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247–259, 2011. DOI: https://doi.org/10.1016/j.isprsjprs.2010.11.001.

    Article  Google Scholar 

  8. X. D. Kang, S. T. Li, J. A. Benediktsson. Spectral-spatial hyperspectral image classification with edge-preserving filtering. IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2666–2677, 2014. DOI: https://doi.org/10.1109/TGRS.2013.2264508.

    Article  Google Scholar 

  9. A. L. Li, A. Y. Qin, Z. W. Shang, Y. Y. Tang. Spectral-spatial sparse subspace clustering based on three-dimensional edge-preserving filtering for hyperspectral image. International Journal of Pattern Recognition and Artificial Intelligence, vol. 33, no. 3, Article number 1955003, 2019. DOI: https://doi.org/10.1142/S0218001419550036.

    Article  Google Scholar 

  10. B. Tu, X. F. Zhang, J. P. Wang, G. Y. Zhang, X. F. Ou. Spectral-spatial hyperspectral image classification via non-local means filtering feature extraction. Sensing and Imaging, vol. 19, no. 1, Article number 11, 2018. DOI: https://doi.org/10.1007/s11220-018-0196-9.

    Article  Google Scholar 

  11. J. S. Xia, L. Bombrun, T. Adalı, Y. Berthoumieu, C. Germain. Spectral-spatial classification of hyperspectral images using ICA and edge-preserving filter via an ensemble strategy. IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4971–4982, 2016. DOI: https://doi.org/10.1109/TGRS.2016.2553842.

    Article  Google Scholar 

  12. T. Zhang, N. Ai, L. Wang, J. Wang, J. Y. Peng. Spectralspatial hyperspectral image classification based on sparse representation and edge preserving filtering. In Proceedings of International Conference on the Frontiers and Advances in Data Science, IEEE, Xi'an, China, pp. 165–170, 2017. DOI: https://doi.org/10.1109/FADS.2017.8253220.

    Google Scholar 

  13. B. G. Cui, X. D. Ma, X. Y. Xie, G. B. Ren, Y. Ma. Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering. Infrared Physics & Technology, vol. 81, pp. 79–88, 2017. DOI: https://doi.org/10.1016/j.infrared.2016.12.010.

    Article  Google Scholar 

  14. X. D. Kang, X. L. Xiang, S. T. Li, J. A. Benediktsson. PCA-based edge-preserving features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 12, pp. 7140–7151, 2017. DOI: https://doi.org/10.1109/TGRS.2017.2743102.

    Article  Google Scholar 

  15. S. T. Li, K. Z. Zhang, Q. B. Hao, P. H. Duan, X. D. Kang. Hyperspectral anomaly detection with multiscale attribute and edge-preserving filters. IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 10, pp. 1605–1609, 2018. DOI: https://doi.org/10.1109/LGRS.2018.2853705.

    Article  Google Scholar 

  16. S. W. Zhong, C. I. Chang, Y. Zhang. Iterative edge preserving filtering approach to hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 1, pp. 90–94, 2019. DOI: https://doi.org/10.1109/LGRS.2018.2868841.

    Article  Google Scholar 

  17. H. Ghassemian. A review of remote sensing image fusion methods. Information Fusion, vol. 32, pp. 75–89, 2016. DOI: https://doi.org/10.1016/j.inffus.2016.03.003.

    Article  Google Scholar 

  18. L. Y. Fang, N. J. He, S. T. Li, A. J. Plaza, J. Plaza. A new spatial-spectral feature extraction method for hyperspectral images using local covariance matrix representation. IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 6, pp. 3534–3546, 2018. DOI: https://doi.org/10.1109/TGRS.2018.2801387.

    Article  Google Scholar 

  19. T. X. Zhang, J. Y. Su, C. J. Liu, W. H. Chen. Potential bands of sentinel-2A satellite for classification problems in precision agriculture. International Journal of Automation and Computing, vol. 16, no. 1, pp. 16–26, 2019. DOI: https://doi.org/10.1007/s11633-018-1143-x.

    Article  Google Scholar 

  20. Q. Zhang, X. Y. Shen, L. Xu, J. Y. Jia. Rolling guidance filter. In Proceedings of the 13th European Conference on Computer Vision, Springer, Zurich, Switzerland, pp. 815–830, 2014. DOI: https://doi.org/10.1007/978-3-319-10578-9_53.

    Google Scholar 

  21. Z. K. Chen, J. J. Jiang, X. W. Jiang, X. P. Fang, Z. H. Cai. Spectral-spatial feature extraction of hyperspectral images based on propagation filter. Sensors, vol. 18, no. 6, Article number 1978, 2018. DOI: https://doi.org/10.3390/s18061978.

    Article  Google Scholar 

  22. J. H. Rick Chang, Y. C. Frank Wang. Propagated image filtering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, USA, pp. 10–18, 2015. DOI: https://doi.org/10.1109/CVPR.2015.7298595.

    Google Scholar 

  23. J. M. Bioucas-Dias, J. M. P. Nascimento. Hyperspectral subspace identification. IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 8, pp. 2435–2445, 2008. DOI: https://doi.org/10.1109/TGRS.2008.918089.

    Article  Google Scholar 

  24. X. Wu, B. Huang, L. Z. Wang, J. Q. Zhang. GPU-based parallel design of the hyperspectral signal subspace identification by minimum error (HySime). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 9, pp. 4400–4406, 2016. DOI: https://doi.org/10.1109/JSTARS.2016.2574876.

    Article  Google Scholar 

  25. B. T. Zhang, X. P. Wang, Y. Shen, T. Lei. Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm methods. International Journal of Automation and Computing, vol. 16, no. 3, pp. 286–296, 2019. DOI: https://doi.org/10.1007/s11633-019-1171-1.

    Article  Google Scholar 

  26. P. Jain, V. Tyagi. A survey of edge-preserving image denoising methods. Information Systems Frontiers, vol. 18, no. 1, pp. 159–170, 2016. DOI: https://doi.org/10.1007/s10796-014-9527-0.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Ayandegan Institute of Higher Education in Tonekabon, Mazandaran, 4681853617, Iran

    Negin Alborzi & Homayoun Beheshti

  2. Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Ayandegan Institute of Higher Education in Tonekabon, Mazandaran, 4681853617, Iran

    Fereshteh Poorahangaryan

Authors
  1. Negin Alborzi
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  2. Fereshteh Poorahangaryan
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  3. Homayoun Beheshti
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Corresponding author

Correspondence to Fereshteh Poorahangaryan.

Additional information

Recommended by Associate Editor De Xu

Negin Alborzi received the B. Eng. degree in computer engineering from Ayandegan Institute of Higher Education in Tonekabon, Iran in 2016. She is currently a Master student in information technology at Department of Computer Engineering Faculty of Electrical and Computer Engineering, Ayandegan Institute of Higher Education in Tonekabon, Iran.

Her research interest is data mining.

Fereshteh Poorahangaryan received the B. Eng. degree in electronics engineering from Amirkabir University, Iran in 2006, and the M. Eng. degree in electronics engineering from Guilan University, Iran in 2009. She received the Ph. D. degree in electronics engineering from the Science and Research Branch of Islamic Azad University, Iran in 2017.

Her research interests include image processing, computer vision, and medical image analysis.

Homayoun Beheshti received the B. Eng. degree in computer engineering from North University, Iran in 2004, and the M. Eng. degree in network engineering from Sharif University of Technology, Iran in 2010. He is currently a Ph. D. degree candidate at Islamic Azad University, Iran.

His research interests include natural language processing (NLP) and data mining.

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Alborzi, N., Poorahangaryan, F. & Beheshti, H. Spectral-spatial Classification of Hyperspectral Images Using Signal Subspace Identification and Edge-preserving Filter. Int. J. Autom. Comput. 17, 222–232 (2020). https://doi.org/10.1007/s11633-019-1188-5

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  • DOI: https://doi.org/10.1007/s11633-019-1188-5

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