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Least Square Based Fast Denoising Approach to Hyperspectral Imagery

  • S. Srivatsa
  • V. Sowmya
  • K. P. Soman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)

Abstract

The presence of noise in hyperspectral images degrades the quality of applications to be carried out using these images. But, since a hyperspectral data consists of numerous bands, the total time requirement for denoising all the bands will be much higher compared to normal RGB or multispectral images. In this paper, a denoising technique based on Least Square (LS) weighted regularization is proposed. It is fast, yet efficient in denoising images. The proposed denoising technique is compared with Legendre-Fenchel (LF) denoising, Wavelet-based denoising, and Total Variation (TV) denoising methods based on computational time requirement and Signal-to-Noise Ratio (SNR) calculations. The experimental results show that the proposed LS-based denoising method gives as good denoising output as LF and Wavelet, but with far lesser time consumption. Also, edge details are preserved unlike in the case of total variation technique.

Keywords

Hyperspectral denoising Least square Legendre-Fenchel Wavelet Total variation Signal-to-noise ratio 

References

  1. 1.
    Qiangqiang Yuan, Liangpei Zhang: Hyperspectral Image Denoising Employing a Spectral Spatial Adaptive Total Variation Model. IEEE Trans. Geosci. Remote Sens., vol 50, issue 10, pp 3660–3677, 2012.Google Scholar
  2. 2.
    Ting Li, Xiao-Mei Chen, Bo Xue, Qian-Qian Li, Guo-Qiang Ni: A total variation denoising algorithm for hyperspectral data. Proceeding of SPIE, The International Society for Optical Engineering, Nov 2010.Google Scholar
  3. 3.
    Hao Yang, Dongyan Zhang, Wenjiang Huang, Zhongling Gao, Xiaodong Yang, Cunjun Li, Jihua Wang: Application and Evaluation of Wavelet-based Denoising Method in Hyperspectral Imagery Data. Springer Berlin Heidelberg, CCTA 2011, Part IIIFIP AICT 369, pp 461–469, 2012.Google Scholar
  4. 4.
    Nikhila Haridas, Aswathy. C, V. Sowmya, Soman. K.P.: Hyperspectral Image Denoising Using Legendre-Fenchel Transform for Improved Sparsity Based Classification. Springer International Publishing. Advances in Intelligent Systems and Computing, vol 384, pp 521–528. Aug 2015.Google Scholar
  5. 5.
    Xiang-Yang Wang, Zhong-Kai Fu: A wavelet-based image denoising using least squares support vector machine. Engineering Applications of Artificial Intelligence. vol 23, issue 6, Sep 2010.Google Scholar
  6. 6.
    Aswathy. C, V. Sowmya, Soman. K.P.: ADMM based Hyperspectral Image Classification improved by Denoising Using Legendre Fenchel Transformation. Indian Journal of Science and Technology. Indian Journal of Science and Technology, vol 8(24), Sep 2015.Google Scholar
  7. 7.
    Linlin Xu, Fan Li, Alexander Wong, David A. Clausi: Hyperspectral Image Denoising Using a Spatial-Spectral Monte Carlo Sampling Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8, issue 6, Mar 2015.Google Scholar
  8. 8.
    Selesnick, Ivan.: Least squares with examples in signal processing. [Online], Mar 2013.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Center for Excellence in Computational Engineering and Networking (CEN), Amrita School of EngineeringAmrita Vishwa Vidyapeetham, Amrita UniversityCoimbatoreIndia
  2. 2.Amrita Vishwa Vidyapeetham, Amrita UniversityCoimbatoreIndia

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