Hyperspectral Image Adaptive Denoising Method based on Band Selection and Elite Atomic Union Dictionary Learning

Volume 14, Number 9, September 2018, pp. 1975-1984
DOI: 10.23940/ijpe.18.09.p5.19751984

Xiaodong Yu^a,b, Hongbin Dong^a, Tian Xia^a, and Xiaohui Li^a

Abstract:

The image noise distribution of each band in a hyperspectral image is complex, and it is difficult for the traditional denoising method to achieve the desired effect. To address this problem, a new hyperspectral denoising method is proposed, based on the selection of the band combined with elite atomic joint dictionary learning. Firstly, the original hyperspectral data is reduced by band selection while retaining the main physical information of the spectrum. Then, the K-SVD dictionary learning is performed on each band of the selected image. Finally, the dictionary of each band learning is selected by the elite atom. This strategy generates a joint dictionary, proposes a dictionary learning denoising algorithm with adaptive dictionary length characteristics, and applies it to hyperspectral noisy images for denoising processing. Experiments on hyperspectral remote sensing images show that the peak signal-to-noise ratio (PSNR) of the image after denoising is improved compared with CFS, CFS-SRNS, and CFS-KSVD.

References: 21

1. M. Zhao, B. An, Y. Wu, B. Chen, and S. Sun, “A Robust Delaunay Triangulation Matching for Multispectral/Multidate Remote Sensing Image Registration,” IEEE Geoscience & Remote Sensing Letters, Vol. 12, No. 4, pp. 711-715, April 2015
2. M. Izadi and P. Saeedi, “Robust Weighted Graph Transformation Matching for Rigid and Nonrigid Image Registration,” IEEE Transactions on Image Processing, Vol. 21, No. 10, pp. 4369-4382, October 2012
3. H. R. Shahdoosti and H. Ghassemian, “Fusion of MS and PAN Images Preserving Spectral Quality,” IEEE Geoscience & Remote Sensing Letters, Vol. 12, No. 3, pp. 611-615, December 2014
4. J. Marcello, A. Medina, and F. Eugenio, “Evaluation of Spatial and Spectral Effectiveness of Pixel-Level Fusion Techniques,” IEEE Geoscience & Remote Sensing Letters, Vol. 10, No. 3, pp. 432-436, October 2013
5. Y. Chen, N. M. Nasrabadi, and T. D. Tran, “Hyperspectral Image Classification via Kernel Sparse Representation,” IEEE Transactions on Geoscience & Remote Sensing, Vol. 51, No. 1, pp. 217-231, January 2013
6. J. Munoz-Mari, D. Tuia, and G. Camps-Valls, “Semisupervised Classification of Remote Sensing Images with Active Queries,” IEEE Transactions on Geoscience & Remote Sensing, Vol. 50, No. 10, pp. 3751-3763, October 2012
7. L. M. Bruce, C. H. Koger, and J. Li, “Dimensionality Reduction of Hyperspectral Data using Discrete Wavelet Transform Feature Extraction,” IEEE Transactions on Geoscience & Remote Sensing, Vol. 40, No. 10, pp. 2331-2338, October 2002
8. L. O. Jimenez and D. A. Landgrebe, “Hyperspectral Data Analysis and Supervised Feature Reduction via Projection Pursuit,” IEEE Transactions on Geoscience & Remote Sensing, Vol. 37, No. 6, pp. 2653-2667, June 1999
9. J. C. Harsanyi and C. I. Chang, “Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach,” IEEE Transactions on Geoscience & Remote Sensing, Vol. 32, No. 4, pp. 779-785, April 1994
10. C. I. Chang, Q. Du, T. L. Sun, and M. L. G. Althouse, “A Joint Band Prioritization and Band-Decorrelation Approach to Band Selection for Hyperspectral Image Classification,” IEEE Transactions on Geoscience & Remote Sensing, Vol. 37, No. 6, pp. 2631-2641, June 1999
11. S. B. Serpico and L. Bruzzone, “A New Search Algorithm for Feature Selection in Hyperspectral Remote Sensing Images,” IEEE Transactions on Geoscience & Remote Sensing, Vol. 39, No. 7, pp. 1360-1367, July 2001
12. F. Samadzadegan, H. Hasani, and T. Schenk, “Simultaneous Feature Selection and SVM Parameter Determination in Classification of Hyperspectral Imagery using Ant Colony Optimization,” Canadian Journal of Remote Sensing, Vol. 38, No. 2, pp. 139-156, February 2012
13. X. U. Ming-Ming, L. P. Zhang, D. U. Bo, and L. F. Zhang, “Supervised Band Selection based on Class Separability for Hyperspectral Image,” Computer Science, 2012
14. M. Elad and M. Aharon, “Image Denoising via Sparse and Redundant Representations over Learned Dictionaries,” IEEE Transactions on Image Processing, Vol. 15, No. 12, pp. 3736-3745, December 2006
15. J. Mairal, M. Elad, and G. Sapiro, “Sparse Representation for Color Image Restoration,” IEEE Transactions on Image Processing, Vol. 17, No. 1, pp. 53-69, January 2007
16. J. M. Zhang, S. X. Gao, X. N. Wang, and C. Center, “Hyperspectral Image Denoising based on Low Rank Dictionary Learning,” Control Engineering of China, 2016
17. Y. Chang, L. Yan, H. Fang, and H. Liu, “Simultaneous Destriping and De-noising for Remote Sensing Images with Unidirectional Total Variation and Sparse Representation,” IEEE Geoscience & Remote Sensing Letters, Vol. 11, No. 6, pp. 1051-1055, June 2014
18. J. Y. Zhang, X. Zhang, X. Liu, and Y. I. Weining, “Investigation on Adaptive De-noising of Remote Sensing Image,” Journal of Atmospheric & Environmental Optics, Vol. 6, No. 5, pp. 368-376, May 2011
19. M. Aharon, M. Elad, and A. Bruckstein, “The K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Transactions on Signal Processing, Vol. 51, No. 11, pp. 4311-4322, November 2006
20. J. H. Wu, Q. B. Song, J. Y. Shen, and J. W. Xie, “Feature Selection Algorithm based on Association Rules,” Pattern Recognition & Artificial Intelligence, 2009
21. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, “Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition,” in Proceedings of Asilomar Conference on Signals, Systems and Computers, Vol. 1, pp. 40-44, 1993

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