Fusion of spectral wavelet and spatial total-variation methods to reduce the noise of hyperspectral images

Document Type : Persian Original Article

Authors

1 school of surveying and geospatial engineering - University of Tehran - Tehran

2 School of surveying and geospatial engineering - University of Tehran - Tehran

Abstract

Hyperspectral images as a source of information can be used for diverse applications in various fields, including target identification, classification, change detection and anomaly detection in urban and non-urban areas. Noise is an inevitable part of a signal which limits the use of hyperspectral images in some applications. Noise removal is one of the most important pre-processing stages in hyperspectral images. In order to remove the noise in hyperspectral images, the data needs to be preprocessed to reduce noise impact on the images. The process and analysis of hyperspectral images is rather complicated because of the high dimensionality of hyperspectral images compared to multispectral remote sensing images. Hyperspectral image cube consist of three dimensions which the first and second dimensions related to the spatial domain and the third one related to the spectral domain which includes more than hundred bands. Most of the methods operate in the spectral domain for noise reduction while in this proposed method, a novel algorithm for reducing noise in hyperspectral images is implemented. The proposed method uses two different algorithms which are applied in two different hyperspectral images in both spatial and spectral domains. These images are Hyperion satellite image and AVIRIS airborne image. In order to reduce noise in the spatial domain, Total Variation (TV) algorithm and in the spectral domain, Wavelet algorithm is used. After the implementation of these methods, the results are fused at the pixel level. For the evaluation of the proposed method, the results were compared with other methods, both qualitatively and quantitatively. Various indices are used to assess the quantitative results which demonstrate the high accuracy of this method.CEI index for Hyperion image is 1.421 and for AVIRIS image is 0.0022. Another index is PSNR which the value for Hyperion image is 33.519 and for AVIRIS image is 22.371.

Keywords

References

[1]        H. S. to Bühne and N. Pettorelli, “Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science,” Methods Ecol. Evol., vol. 9, no. 4, pp. 849–865, 2018.
[2]        C.-I. Chang, Hyperspectral imaging: techniques for spectral detection and classification, vol. 1. Springer Science & Business Media, 2003.
[3]        G. Pariani et al., “Compressive sampling for multispectral imaging in the vis-NIR-TIR: optical design of space telescopes,” in Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave, 2018, vol. 10698, p. 106985O.
[4]        Q. Yuan, L. Zhang, and H. Shen, “Hyperspectral Image Denoising With a Spatial-Spectral View Fusion Strategy.,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2314–2325, 2014.
[5]        L. Sun and J. Luo, “Junk band recovery for hyperspectral image based on curvelet transform,” J. Cent. South Univ. Technol., vol. 18, no. 3, pp. 816–822, 2011.
[6]        G. Lantzanakis, Z. Mitraka, and N. Chrysoulakis, “Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery,” in Perspectives on Atmospheric Sciences, Springer, 2017, pp. 255–261.
[7]        A. Zelinski and V. Goyal, “Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation,” in Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on, 2006, pp. 387–390.
[8]        E. Martel et al., “Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons,” Remote Sens., vol. 10, no. 6, p. 864, 2018.
[9]        P. T. Fazila and D. A. K. Mohideen, “A novel approach for hyperspectral image mixed noise reduction based on improved K-SVD algorithm,” Int. J. Emerg. Technol. Adv. Eng, vol. 4, no. 3, pp. 76–83, 2014.
[10]     H. Zhang, W. He, L. Zhang, H. Shen, and Q. Yuan, “Hyperspectral image restoration using low-rank matrix recovery,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 4729–4743, 2014.
[11]     W. Wei, L. Zhang, Y. Jiao, C. Tian, C. Wang, and Y. Zhang, “Intracluster Structured Low-Rank Matrix Analysis Method for Hyperspectral Denoising,” IEEE Trans. Geosci. Remote Sens., no. 99, pp. 1–15, 2018.
[12]     P. Scheunders and S. De Backer, “Wavelet denoising of multicomponent images using Gaussian scale mixture models and a noise-free image as priors,” IEEE Trans. Image Process., vol. 16, no. 7, pp. 1865–1872, 2007.
[13]     B. Rasti, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, “Hyperspectral image denoising using first order spectral roughness penalty in wavelet domain,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol. 7, no. 6, pp. 2458–2467, 2014.
[14]     H. Yang et al., “Application and evaluation of wavelet-based denoising method in hyperspectral imagery data,” in International Conference on Computer and Computing Technologies in Agriculture, 2011, pp. 461–469.
[15]     T. Lin and S. Bourennane, “Survey of hyperspectral image denoising methods based on tensor decompositions,” EURASIP J. Adv. Signal Process., vol. 2013, no. 1, p. 186, Dec. 2013.
[16]     L. Xu, F. Li, A. Wong, and D. A. Clausi, “Hyperspectral image denoising using a spatial--spectral monte carlo sampling approach,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 6, pp. 3025–3038, 2015.
[17]     A. Lam, I. Sato, and Y. Sato, “Denoising hyperspectral images using spectral domain statistics,” in Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, pp. 477–480.
[18]     H. Zhang, “Hyperspectral image denoising with cubic total variation model,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 7, pp. 95–98, 2012.
[19]     L. Sun, B. Jeon, Z. Wu, and L. Xiao, “Hyperspectral Denoising Via Cross Total Variation-Regularized Unidirectional Nonlocal Low-Rank Tensor Approximation,” in 2018 25th IEEE International Conference on Image Processing (ICIP), 2018, pp. 2900–2904.
[20]     https://earthexplorer.usgs.gov/
[21]     http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
[22]     L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D nonlinear Phenom., vol. 60, no. 1–4, pp. 259–268, 1992
[23]     J. J. Benedetto, W. Czaja, M. Ehler, C. Flake, and M. Hirn, “Wavelet packets for multi-and hyper-spectral imagery,” in Wavelet Applications in Industrial Processing VII, 2010, vol. 7535, p. 753508.
[24]     P. S. Addison, The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC press, 2017.
[25]     N. Verma and A. K. Verma, “Performance analysis of wavelet thresholding methods in denoising of audio signals of some Indian Musical Instruments,” Int. J. Eng. Sci. Technol., vol. 4, no. 5, pp. 2040–2045, 2012.
[26]     S. Li, X. Kang, L. Fang, J. Hu, and H. Yin, “Pixel-level image fusion: A survey of the state of the art,” Inf. Fusion, vol. 33, pp. 100–112, 2017.
[27]     H. Peng, “Automatic Denoising and Unmixing in Hyperspectral Image Processing,” 2014.
Journal of Soft Computing and Information Technology
Volume 7, Issue 2 - Serial Number 2
Fall & Winter
January 2019
Pages 29-42

Files

Share

How to cite

Statistics