Current Optics and Photonics

Optical Society of Korea (한국광학회)
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A Spectral-spatial Cooperative Noise-evaluation Method for Hyperspectral Imaging

  • Zhou, Bing (Department of Opto-electronics Army Engineering University) ;
  • Li, Bingxuan (Department of Opto-electronics Army Engineering University) ;
  • He, Xuan (Department of Opto-electronics Army Engineering University) ;
  • Liu, Hexiong (Department of Opto-electronics Army Engineering University)
  • Received : 2020.06.29
  • Accepted : 2020.10.05
  • Published : 2020.12.25
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Abstract

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

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References

  1. P. Du, J. Xia, W. Zhang, K. Tan, Y. Li, and S. Liu, "Multiple classifier system for remote sensing image classification: a review," Sensors 12, 4764-4792 (2012). https://doi.org/10.3390/s120404764
  2. W. Zhang, F. Tian, Z. Zhao, A. Song, and L. Zhang, "Research on the technology of alternative continuous wide spectral spatial heterodyne spectrometer," Curr. Opt. Photon. 1, 295-307 (2017). https://doi.org/10.3807/COPP.2017.1.4.295
  3. N. Fujimoto, Y. Takahashi, T. Moriyama, M. Shimada, H. Wakabayashi, Y. Nakatani, and S. Obayashi, "Evaluation of SPOT HRV image data received in Japan," in Proc. Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium (Vancouver, Canada, Jul. 1989), pp. 463-466.
  4. B. R. Corner, R. M. Narayanan, and S. E. Reichenbach, "Noise estimation in remote sensing imagery using data masking," Int. J. Remote Sens. 24, 689-702 (2003). https://doi.org/10.1080/01431160210164271
  5. L. Alparone, M. Selva, B. Aiazzi, S. Baronti, F. Butera, and L. Chiarantini, "Signal-dependent noise modelling and estimation of new-generation imaging spectrometers," in Proc. First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Grenoble, France, Aug. 2009), pp. 1-4.
  6. P. Fu, X. Sun, and Q. Sun, "Estimation of signal-dependent and -independent noise from hyperspectral images using a wavelet-based superpixel model," Remote Sens. Lett. 9, 906-915 (2018). https://doi.org/10.1080/2150704x.2018.1492171
  7. Z. Tang, G. Fu, J. Chen, and L. Zhang, "A unified model of noise estimation, band rejection, and de-noising for hyperspectral images," Int. J. Remote Sens. 37, 1319-1348 (2016). https://doi.org/10.1080/01431161.2016.1148286
  8. L. Sun, B. Li, and Y. Nian, "Superpixel-based mixed noise estimation for hyperspectral images using multiple linear regression," Remote Sens. 12, 1324 (2020). https://doi.org/10.3390/rs12081324
  9. A. Mahmood, A. Robin, and M. Sears, "Modified residual method for estimation of signal dependent noise in hyperspectral images," in Proc. 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Amsterdam, Netherlands, Sep. 2018), pp. 1-5.
  10. P. Fu, Q.-S. Sun, and Z.-X. Ji, "A spectral-spatial information based approach for the mixed noise estimation from hyperspectral remote sensing images," J. Infrared Milim. Waves 34, 236-242 (2015).
  11. Y. Dian, Z. Li, and Y. Pang, "Spectral and texture features combined for forest tree species classification with airborne hyperspectral imagery," J. Indian Soc. Remote Sens. 43, 101-107 (2015). https://doi.org/10.1007/s12524-014-0392-6
  12. H. Jie, H. Zhi, L. Jun, L. He, and Y. Wang, "3D-Gabor inspired multiview active learning for spectral-spatial hyperspectral image classification," Remote Sens. 10, 1070 (2018). https://doi.org/10.3390/rs10071070
  13. J. M. Haut, M. Paoletti, J. Plaza, and A. Plaza, "Cloud implementation of the K-means algorithm for hyperspectral image analysis," J. Supercomput. 73, 514-529 (2017). https://doi.org/10.1007/s11227-016-1896-3
  14. P. Fu, C. Li, Y. Xia, Z. Ji, Q. Sun, W. Cai, and D. D. Feng, "Adaptive noise estimation from highly textured hyperspectral images," Appl. Opt. 53,7059-7071 (2014). https://doi.org/10.1364/AO.53.007059
  15. S. Sensen, J. Zhenhong, Y. Jie, and N. Kasabov, "Image segmentation algorithm of minimum spanning tree combined with Ostu threshold method," Comput. Eng. Appl. 9, 178-183 (2019).
  16. W. He, L. Zhang, L. Zhang, and H. Shen, "Hyperspectral image denoising via noise-adjusted iterative low-rank matrix approximation," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 3050-3061 (2015). https://doi.org/10.1109/JSTARS.2015.2398433