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Large-scale hyperspectral image compression via sparse representations based on online learning Cover

Large-scale hyperspectral image compression via sparse representations based on online learning

By: İrem Ülkü and  Ersin Kizgut  
Open Access
|Mar 2018

References

  1. Beck, A. and Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences 2(1): 183-202.10.1137/080716542
  2. Bioucas-Dias, J.M. and Figueiredo, M.A. (2007). A new twist: Two-step iterative shrinkage/thresholding algorithms for image restoration, IEEE Transactions on Image Processing 16(12): 2992-3004.10.1109/TIP.2007.909319
  3. Boyd, S., Parikh, N., Chu, E., Peleato, B. and Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends in Machine Learning 3(1): 1-122.10.1561/2200000016
  4. Boyd, S. and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press, Cambridge. 10.1017/CBO9780511804441
  5. Charles, A.S., Olshausen, B.A. and Rozell, C.J. (2011). Learning sparse codes for hyperspectral imagery, IEEE Journal of Selected Topics in Signal Processing 5(5): 963-978.10.1109/JSTSP.2011.2149497
  6. Chen, S.S., Donoho, D.L. and Saunders, M.A. (2001). Atomic decomposition by basis pursuit, SIAM Review 43(1): 129-159.10.1137/S003614450037906X
  7. Donoho, D.L., Tsaig, Y., Drori, I. and Starck, J.L. (2012). Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit, IEEE Transactions on Information Theory 58(2): 1094-1121.10.1109/TIT.2011.2173241
  8. Du, Q. and Fowler, J.E. (2007). Hyperspectral image compression using JPEG2000 and principal component analysis, IEEE Geoscience and Remote Sensing Letters 4(2): 201-205.10.1109/LGRS.2006.888109
  9. Fowler, J.E. (2009). Compressive-projection principal component analysis, IEEE Transactions on Image Processing 18(10): 2230-2242.10.1109/TIP.2009.202508919520637
  10. Friedlander, M. and Saunders, M. (2012). A dual active-set quadratic programming method for finding sparse least-squares solutions, Online, University of British Columbia, Vancouver, BC, http://web.stanford.edu/group/SOL/software/asp/bpdual.pdf.
  11. Gong, P., Zhang, C., Lu, Z., Huang, J. and Ye, J. (2013). A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems, 30th International Conference on Machine Learning (ICML), Atlanta, GA, USA, pp. 37-45.
  12. Hou, Y. and Zhang, Y. (2014). Effective hyperspectral image block compressed sensing using three-dimensional wavelet transform, IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada, pp. 2973-2976.
  13. Ji, S., Xue, Y. and Carin, L. (2008). Bayesian compressive sensing, IEEE Transactions on Signal Processing 56(6): 2346-2356.10.1109/TSP.2007.914345
  14. Kim, S.J., Koh, K., Lustig, M., Boyd, S. andGorinevsky, D. (2007). An interior-point method for large-scale-regularized least squares, IEEE Journal of Selected Topics in Signal Processing 1(4): 606-617.10.1109/JSTSP.2007.910971
  15. Mairal, J., Bach, F., Ponce, J. and Sapiro, G. (2010). Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research 11: 19-60.
  16. Mallat, S.G. and Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing 41(12): 3397-3415.10.1109/78.258082
  17. Needell, D. and Vershynin, R. (2009). Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit, Foundations of Computational Mathematics 9(3): 317-334.10.1007/s10208-008-9031-3
  18. Nowak, R.D. and Wright, S.J. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of Selected Topics in Signal Processing 1(4): 586-597.10.1109/JSTSP.2007.910281
  19. Nowicki, A., Grochowski, M. and Duzinkiewicz, K. (2012). Data-driven models for fault detection using kernel PCA: A water distribution system case study, International Journal of Applied Mathematics and Computer Science 22(4): 939-949, DOI: 10.2478/v10006-012-0070-1.10.2478/v10006-012-0070-1
  20. Olshausen, B.A. and Field, D.J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by v1?, Vision Research 37(23): 3311-3325.10.1016/S0042-6989(97)00169-7
  21. Panek, D., Skalski, A., Gajda, J. and Tadeusiewicz, R. (2015). Acoustic analysis assessment in speech pathology detection, International Journal of Applied Mathematics and Computer Science 25(3): 631-643, DOI: 10.1515/amcs-2015-0046.10.1515/amcs-2015-0046
  22. Parikh, N. and Boyd, S.P. (2014). Proximal algorithms, Foundations and Trends in Optimization 1(3): 127-139. 10.1561/2400000003
  23. Penna, B., Tillo, T. and Olmo, G. (2007). Transform coding techniques for lossy hyperspectral data compression, IEEE Transactions on Geoscience and Remote Sensing 45(5): 1408-1421.10.1109/TGRS.2007.894565
  24. Reed, S.I. and Yu, X. (1990). Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Transactions on Acoustics, Speech, and Signal Processing 38(10): 1760-1770.10.1109/29.60107
  25. Tropp, J.A. and Gilbert, A.C. (2007). Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on Information Theory 53(12): 4655-4666.10.1109/TIT.2007.909108
  26. Ülkü, İ. and Töreyin, B.U. (2015a). Sparse coding of hyperspectral imagery using online learning, Signal, Video and Image Processing 9(4): 959-966.10.1007/s11760-015-0753-9
  27. Ülkü, İ. and Töreyin, B.U. (2015b). Sparse representations for online-learning-based hyperspectral image compression, Applied Optics 54(29): 8625-8631.10.1364/AO.54.00862526479796
  28. Wang, J., Kwon, S. and Shim, B. (2012). Generalized orthogonal matching pursuit, IEEE Transactions on Signal Processing 60(12): 6202-6216.10.1109/TSP.2012.2218810
  29. Wang, Z., Nasrabadi, N.M. and Huang, T.S. (2014). Spatial-spectral classification of hyperspectral images using discriminative dictionary designed by learning vector quantization, IEEE Transactions on Geoscience and Remote Sensing 52(8): 4808-4822.10.1109/TGRS.2013.2285049
  30. Wright, J., Yang, A.Y., Ganesh, A. and Sastry, S.S. (2009). Robust face recognition via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2): 210-227.10.1109/TPAMI.2008.7919110489
  31. Yang, A.Y., Zhou, Z., Balasubramanian, A.G., Sastry, S.S. and Ma, Y. (2013). Fast-minimization algorithms for robust face recognition, IEEE Transactions on Image Processing 22(8): 3234-3246.10.1109/TIP.2013.226229223674456
  32. Yang, J., Peng, Y., Xu, W. and Dai, Q. (2009). Ways to sparse representation: An overview, Science in China F: Information Sciences 52(4): 675-703.10.1007/s11432-009-0045-5
  33. Zhang, Z., Xu, Y., Yang, J., Li, X. and Zhang, D. (2015). A survey of sparse representation: Algorithms and applications, IEEE Access 3: 490-530.10.1109/ACCESS.2015.2430359
  34. Zuo, W., Meng, D., Zhang, L., X.F. and Zhang, D. (2013). A generalized iterated shrinkage algorithm for non- convex sparse coding, Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, pp. 217-224.10.1109/ICCV.2013.34
DOI: https://doi.org/10.2478/amcs-2018-0015 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 197 - 207
Submitted on: Feb 10, 2017
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Accepted on: Oct 16, 2017
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Published on: Mar 31, 2018
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 İrem Ülkü, Ersin Kizgut, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.