Volume 44 Issue 1
Feb.  2015
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Xu Dong, Sun Lei, Luo Jianshu. Denoising of hyperspectral remote sensing imagery using NAPCA and complex wavelet transform[J]. Infrared and Laser Engineering, 2015, 44(1): 327-334.
Citation: Xu Dong, Sun Lei, Luo Jianshu. Denoising of hyperspectral remote sensing imagery using NAPCA and complex wavelet transform[J]. Infrared and Laser Engineering, 2015, 44(1): 327-334.

Denoising of hyperspectral remote sensing imagery using NAPCA and complex wavelet transform

  • Received Date: 2014-05-07
  • Rev Recd Date: 2014-06-10
  • Publish Date: 2015-01-25
  • A new denoising algorithm was proposed to keep the fine features of hyperspectral remote sensing imagery effectively. Firstly, the noise-adjust principal components analysis (NAPCA) was performed on the hyperspectral datacube. Then output channels of the low-energy NAPCA were transformed into the wavelet domain by 2-D complex wavelet transform(CWT). The BivaShrink function was used to shrink the wavelet coefficients. And then 1-D CWT denoising method was used to remove the noise of the each spectrum of the low-energy NAPCA datacube. The AVIRIS images Jasper Ridge, Lunar Lake and Low Altitude were used for the simulated experiment. Compared with the HSSNR and the PCABS, the signal-to-noise ratio (SNR) is improved by 4.3-7.8 dB and 0.8-0.9 dB via the proposed method in this paper, which shows that the proposed method is feasible. It is shown that the proposed method is correctable and available according to the experimental results of the real datacube OMIS.
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    [12] Chang Weiwei, Guo Lei, Liu Kun, et al. Denoising of hyperspectral data based on contourlet transform and principal component analysis [J]. Journal of Electronics Information Technolog, 2009, 31(12): 2892-2896. (in Chinese) 常威威, 郭雷, 刘坤, 等. 基于Contourlet 变换和主成分分析的高光谱数据噪声消除方法[J]. 电子与信息学报, 2009, 31(12): 2892-2896.
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Denoising of hyperspectral remote sensing imagery using NAPCA and complex wavelet transform

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

Abstract: A new denoising algorithm was proposed to keep the fine features of hyperspectral remote sensing imagery effectively. Firstly, the noise-adjust principal components analysis (NAPCA) was performed on the hyperspectral datacube. Then output channels of the low-energy NAPCA were transformed into the wavelet domain by 2-D complex wavelet transform(CWT). The BivaShrink function was used to shrink the wavelet coefficients. And then 1-D CWT denoising method was used to remove the noise of the each spectrum of the low-energy NAPCA datacube. The AVIRIS images Jasper Ridge, Lunar Lake and Low Altitude were used for the simulated experiment. Compared with the HSSNR and the PCABS, the signal-to-noise ratio (SNR) is improved by 4.3-7.8 dB and 0.8-0.9 dB via the proposed method in this paper, which shows that the proposed method is feasible. It is shown that the proposed method is correctable and available according to the experimental results of the real datacube OMIS.

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