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Spectral Tensor Synthesis Analysis for Hyperspectral Image Spectral–Spatial Feature Extraction

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Abstract

Feature extraction is a preprocessing step for hyperspectral image classification. Principal component analysis only uses the spectral information, but it does not use spatial information of a hyperspectral image. Both spatial and spectral information are used when hyperspectral image is modelled as tensor, that is, decreasing the noise on spatial dimension and reducing the dimension on a spectral dimension at the same time. However, in this model, a hyperspectral image is modelled only as a data cube. The factors affecting the spectral features of ground objects is not considered and these factors are barely distinguished. This means that further improving classification is very difficult. Therefore, a new model on hyperspectral image is proposed by the authors. In the new model, many factors that impact the spectral features of ground objects are synthesized as the within-class factor. The within-class factor, the class factor and the pixel spectral are selected as a mode, respectively. The pixel spectrals in the training set are modelled as a third-order tensor. The experiment results indicate that the new method improves the classification compared with the previous methods.

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References

  • Akbari, D. (2017). Improving spectral–spatial classification of hyperspectral imagery using spectral dimensionality reduction based on weighted genetic algorithm. Journal of the Indian Society of Remote Sensing, 45(6), 927–937.

    Article  Google Scholar 

  • Bourennane, S., & Fossati, C. (2015). Dimensionality reduction and coloured noise removal from hyperspectral images. Remote Sensing Letters, 6, 854–863.

    Article  Google Scholar 

  • Gao, Y., Wang, X., Cheng, Y., & Wang, Z. (2015). Dimensionality reduction for hyperspectral data based on class-aware tensor neighborhood graph and patch alignment. IEEE Transactions on Neural Networks, 26, 1582–1593.

    Article  Google Scholar 

  • Landgrebe, D. (2003). Signal theory methods in multispectral remote sensing. New Jersey: Wiley.

    Book  Google Scholar 

  • Lathauwer, D., Moor, B. D., & Vandewalle, J. (2000). On the best rank-(r1, rn) approximation of higher-order tensors. SIAM Journal of Matrix Analysis and Applications, 21, 1324–1342.

    Article  Google Scholar 

  • Letexier, D., Bourennane, S., & Blanc-Talon, J. (2008). Nonorthogonal tensor matricization for hyperspectral image filtering. IEEE Geoscience and Remote Sensing Letters, 5(1), 3–7.

    Article  Google Scholar 

  • Li, C., Ma, Y., Huang, J., Mei, X., & Ma, J. (2015). Hyperspectral image denoising using the robust low-rank tensor recovery. Journal of the Optical Society of America A: Optics, Image Science, and Vision, 32, 1604–1612.

    Article  Google Scholar 

  • Ma, L., et al. (2016). Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification. Pattern Recognition Letters, 83, 133–142.

    Article  Google Scholar 

  • Michael, D. F., & Mersereau, R. M. (2005). On the impact of PCA dimension reduction for hyperspectral detection of difficult targets. IEEE Geoscience and Remote Sensing Letters, 2, 192–195.

    Article  Google Scholar 

  • Muti, D., & Bourennane, S. (2003). Multidimensional signal processing using lower-rank tensor approximation. IEEE ICASSP, 3, 457–460.

    Google Scholar 

  • Muti, D., & Bourennane, S. (2005). Multidimensional filtering based on a tensor approach. Signal Process, 85, 2338–2353.

    Article  Google Scholar 

  • Pu, R., & Gong, P. (2010). Hyperspectral remote sensing and its applications. Beijing, China: Higher Education Press.

    Google Scholar 

  • Qiao, X., Yuan, D., & Li, H. (2017). Urban shadow detection and classification using hyperspectral image. Journal of the Indian Society of Remote Sensing, 45(6), 945–952.

    Article  Google Scholar 

  • Renard, N., & Bourennane, S. (2008). Improvement of target detection methods by multiway filtering. IEEE Transactions on Geoscience and Remote Sensing, 46, 2407–2417.

    Article  Google Scholar 

  • Renard, N., Bourennane, S., & Blanc, J. (2008). Denoising and dimensionality reduction using multilinear tools for hyperspectral image. IEEE Geoscience and Remote Sensing Letters, 5, 138–142.

    Article  Google Scholar 

  • Tao, D., & Huang, X. (2013). Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 51, 242–256.

    Article  Google Scholar 

  • Tong, Q., Zhang, B., & Zheng, L. (2006). Hyperspectral remote sensing: principles, techniques and applications. Beijing, China: Higher Education Press.

    Google Scholar 

  • Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika, 31, 279–311.

    Article  Google Scholar 

  • Zhong, Z., Fan, B., Duan, J., Wang, L., Ding, K., Xiang, S., et al. (2015). Discriminant tensor spectral-spatial feature extraction for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 12, 1028–1032.

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation (Grant No. 61272285) and Program for Changjiang Scholars and Innovative Research Team in University (IRT13090).

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Authors and Affiliations

  1. School of Electronics and Information, Space Optics Lab, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Ronghua Yan & Jinye Peng

  2. Xi’an Institute of Optics and Precision Mechanics, CAS, Xi’an, 710119, People’s Republic of China

    Ronghua Yan & Desheng Wen

  3. School of Information and Technology, Northwest University, Xi’an, 710127, People’s Republic of China

    Jinye Peng

  4. Xi’an-Janssen Pharmaceutical Ltd, Xi’an, 710043, People’s Republic of China

    Dongmei Ma

Authors
  1. Ronghua Yan
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  2. Jinye Peng
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  3. Dongmei Ma
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  4. Desheng Wen
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Correspondence to Ronghua Yan.

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Yan, R., Peng, J., Ma, D. et al. Spectral Tensor Synthesis Analysis for Hyperspectral Image Spectral–Spatial Feature Extraction. J Indian Soc Remote Sens 47, 91–100 (2019). https://doi.org/10.1007/s12524-018-0873-0

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  • DOI: https://doi.org/10.1007/s12524-018-0873-0

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