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Junk band recovery for hyperspectral image based on curvelet transform

Journal of Central South University volume 18, pages 816–822 (2011)Cite this article

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Abstract

Under consideration that the profiles of bands at close wavelengths are quite similar and the curvelets are good at capturing profiles, a junk band recovery algorithm for hyperspectral data based on curvelet transform is proposed. Both the noisy bands and the noise-free bands are transformed via curvelet band by band. The high frequency coefficients in junk bands are replaced with linear interpolation of the high frequency coefficients in noise-free bands, and the low frequency coefficients remain the same to keep the main spectral characteristics from being distorted. Junk bands then are recovered after the inverse curvelet transform. The performance of this method is tested on the hyperspectral data cube obtained by airborne visible/infrared imaging spectrometer (AVIRIS). The experimental results show that the proposed method is superior to the traditional denoising method BayesShrink and the art-of-state Curvelet Shrinkage in both roots of mean square error (RMSE) and peak-signal-to-noise ratio (PSNR) of recovered bands.

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References

  1. GREEN R O, EASTWOOD M L, SARTURE C M, CHRIEN T G, ARONSSON M, CHIPPENDALE B J, FAUST J A, PAVRI B E, CHOVIT C, SOLIS M, OLAH M R. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS) [J]. Remote Sensing of Environment, 1998, 65: 227–248.

    Article  Google Scholar 

  2. TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen. Hyperspectral remote sensing-principle: Technology and application [M]. Beijing: Higher Education Press, 2006: 2–6. (in Chinese)

    Google Scholar 

  3. MATES D M, ZWICH H, JOLLY G, SCHULTEN D. System studies of a small satellite hyperspectral mission: Data acceptability [R]. Canada: Can Gov Contract Rep, 2004.

    Google Scholar 

  4. OTHMAN H, QIAN S E. Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(2): 397–408.

    Article  Google Scholar 

  5. KAEWPIJIT S, LEMOIGNE J, ELGHAZAWI T. A wavelet-based PCA reduction for hyperspectral Imagery [C]// IEEE International Geoscience and Remote Sensing Symposium. Canada, 2002: 2581–2583.

  6. SHAH C A, WATANACHATURAPORN P, VARSHNEY P K. Some recent results on hyperspectral image classification [C]// IEEE Workshop on Advance in Technology for Analysis of Remotely Sensed Data. USA, 2003: 346–353.

  7. DONOHO D L. De-noising by soft-thresholding [J]. IEEE Transactions on Information Theory, 1995, 41: 613–627.

    Article  MathSciNet  Google Scholar 

  8. CHANG S G, YU B, VETTERLI M. Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Transactions on Image Process, 2000, 9(9): 1532–1546.

    Article  MathSciNet  Google Scholar 

  9. PORILLA J, STRELA V, WAINWRIGHT M J, SIMONCELLI E P. Image denoising using scale mixtures of Gaussians in the wavelet domain [J]. IEEE Transactions on Image Processing, 2003, 12(11): 1338–1351.

    Article  MathSciNet  Google Scholar 

  10. SHUI P. Image denoising using 2-D separable oversampled DFT modulated filter banks [J]. IET Image processing, 2009, 3(3): 163–173.

    Article  MathSciNet  Google Scholar 

  11. ZELINSKI A C, GOYAL V K. Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation [C]// IEEE International Geoscience and Remote Sensing Symposium. USA, 2006: 387–390.

  12. WU Chuan-qing, TONG Qing-xi, ZHENG Lan-fen. De-noise of hyperspectral image based on wavelet transformation [J]. Remote Sensing Information, 2005(4): 10–12. (in Chinese)

  13. CANDES E J, DONOHO D L. Curvelets—A surprisingly effective non-adaptive representation for objects with edges [C]// Proceedings of the in Curve and Surface Fitting. USA, 2000: 105–120.

  14. CANDES E J, DONOHO D L. New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities [J]. Comm Pure Appl Math, 2004, 57(2): 219–266.

    Article  MathSciNet  Google Scholar 

  15. CANDES E J, DEMANET L, DONOHO D L. Fast discrete curvelet transforms [J]. Multiscale Model Simul, 2006, 5(3): 861–899.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Sciences, National University of Defense and Technology, Changsha, 410073, China

    Lei Sun  (孙蕾) & Jian-shu Luo  (罗建书)

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  1. Lei Sun  (孙蕾)
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  2. Jian-shu Luo  (罗建书)
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Corresponding author

Correspondence to Lei Sun  (孙蕾).

Additional information

Foundation item: Project(10871231) supported by the National Natural Science Foundation of China

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Sun, L., Luo, Js. Junk band recovery for hyperspectral image based on curvelet transform. J. Cent. South Univ. Technol. 18, 816–822 (2011). https://doi.org/10.1007/s11771-011-0767-6

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  • DOI: https://doi.org/10.1007/s11771-011-0767-6

Key words

  • hyperspectral image
  • curvelet transform
  • junk band
  • denosing