ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Compressive Sensing Approach to Hyperspectral Image Compression


Affiliations
1 Department of Electronics and Communication Engineering, NITTE Meenakshi Institute of Technology, India
     

   Subscribe/Renew Journal


Hyperspectral image (HSI) processing is one of the key processes in satellite imaging applications. Hyperspectral imaging spectrometers collect huge volumes of data since the image is captured across different wavelength bands in the electromagnetic spectrum. As a result, compression of hyperspectral images is one of the active area in research community from many years. The research work proposes a new compressive sensing based approach for the compression of hyperspectral images called SHSIR (Sparsification of hyperspectral image and reconstruction). The algorithm computes the coefficients of fractional abundance map in matrix setup, which is used to reconstruct the hyperspectral image. To optimize the problem with non-smooth term existence along with large dimensionality, Bregman iterations method of multipliers is used, which converts the difficult optimization problem into simpler cyclic sequence problem. Experimental result demonstrates the supremacy of the proposed method over other existing techniques.

Keywords

Hyperspectral Image, Image Compression, Compressive Sensing.
Subscription Login to verify subscription
User
Notifications
Font Size

  • D.C. Heinz and C.I. Chang, “Fully Constrained Least-Squares Linear-Spectral Mixture Analysis Approach for Material Quantification in the Hyperspectral Imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 3, pp. 529-545, 2001.

  • F.A. Kruse, J.W. Boardman and J.F. Huntington, “Comparison of Airborne Hyperspectral Data and EO-1 Hyperion for Mineral Mapping”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 6, pp. 1388-1400, 2003.

  • E.K. Hege, D. O’Connell, W. Johnson, S. Basty and E.L. Dereniak, “Hyperspectral Imaging for Astronomy and Space Surviellance”, Proceedings of 48th Annual Meeting Optical Science and Technology, pp. 380-391, 2004.

  • M.E. Schaepman, S.L. Ustin, A.J. Plaza, T.H. Painter, J. Verrelst and S. Liang, “Earth System Science related Imaging Spectroscopy-An Assessment”, Remote Sensing of Environment, Vol. 113, pp. 123-137, 2009.

  • S.V. Panasyuk et al., “Medical Hyperspectral Imaging to Facilitate Residual Tumor Identification during Surgery”, Cancer Biology and Therapy, Vol. 6, No. 3, pp. 439-446, 2007.

  • N. Gat, “Imaging Spectroscopy using Tunable Filters: A Review”, Proceedings of SPIE, Vol. 4056, pp. 50-64, 2000.

  • K.S. Gunasheela and H S Prasantha, “Satellite Image Compression-Detailed Survey of the Algorithms”, Proceedings of International Conference on Cognition and Recognition, Vol. 14, pp. 187-198, 2017.

  • E. Candes and T. Tao, “Decoding by Linear Programming”, IEEE Transactions on Information Theory, Vol. 51, No. 12, pp. 4203-4215, 2005.

  • S. Alliney and S.A. Ruzinsky, “An Algorithm for the Minimization of Mixed l1 and l2 Norms with Application to Bayesian Estimation”, IEEE Transactions on Signal Processing, Vol. 42, pp. 618-627, 1994.

  • R. Tibshirani, “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society: Series B, Vol. 58, pp. 267-288, 1996.

  • L.I. Rudin, S. Osher and E. Fatemi, “Nonlinear Total Variation based Noise Removal Algorithms”, Physica D: Nonlinear Phenomena, Vol. 60, No. 1-4, pp. 259-268, 1992.

  • A. Chambolle, “An Algorithm for Total Variation Minimization and Applications”, Journal of Mathematical Imaging and Vision, Vol. 20, pp. 89-97, 2004.

  • J.M. Bioucas-Dias and M.A.T. Figueiredo, “A New Twist: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration”, IEEE Transactions on Image Processing, Vol. 16, No. 12, pp. 2992-3004, 2007.

  • J.L. Starck, M. Elad and D.L. Donoho, “Image Decomposition via the Combination of Sparse Representations and a Variational Approach”, IEEE Transactions on Image Processing, Vol. 14, No. 7, pp. 1570-1582, 2005.

  • A. Beck and M. Teboulle, “Fast Gradient-based Algorithms for Constrained Total Variation image Denoising and Deblurring Problems”, IEEE Transactions on Image Processing, Vol. 18, No. 11, pp. 2419-2434, 2009.

  • M.V. Afonso, J.M. Bioucas Dias and M.A.T. Figueiredo, “Fast Image Recovery using Variable Splitting and Constrained Optimization”, IEEE Transactions on Image Processing, Vol. 19, No. 9, pp. 2345-2356, 2010.

  • M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. Figueiredo, “An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems”, IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 681-695, 2011.

  • T. Goldstein and S. Osher, “The Split Bregman Method for L1-Regularized Problems”, SIAM Journal on Imaging Sciences, Vol. 2, pp. 323-343, 2009.

  • J.M. Bioucas-Dias, M.A.T. Figueiredo and J.P. Oliveira, “Total Variation-based Image Deconvolution: A Majorization-Minimization Approach”, Proceedings of International Conference on Acoustics Speech and Signal Processing, pp. 1-4, 2006.

  • Chengbo Li, T. Sun, K.F. Kelly and Y. Zhang, “A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing”, IEEE Transactions on Image Processing, Vol. 21, No. 3, pp. 1200-1210, 2012.

  • G. Martin, J.M. Bioucas-Dias and A. Plaza, “HYCA: A New Technique for Hyperspectral Compressive Sensing”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 5, pp. 2819-2831, 2015.

  • G. Martin, J.M. Bioucas-Dias and A. Plaza, “Hyperspectral Coded Aperture (HYCA): A New Technique for Hyperspectral Compressive Sensing”, Proceedings of 21st European Signal Processing Conference, pp. 1381-1384, 2012.

  • G. Martin, J.M.B. Dias and A.J. Plaza, “A New Technique for Hyperspectral Compressive Sensing using Spectral Unmixing”, Proceedings of International Conference on Society for Optics and Photonics, pp. 551-556, 2012.

  • Chein I Chang, Chao-Cheng Wu and C.T. Tsai, “Random N-finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery”, IEEE Transactions on Image Processing, Vol. 20, No. 3, pp. 641-656, 2011.

  • K.E. Themelis, A.A. Rontogiannis and K.D. Koutroumbas, “A Novel Hierarchical Bayesian Approach for Sparse Semisupervised Hyperspectral Unmixing”, IEEE Transactions on Signal Processing, Vol. 60, No. 2, pp. 585-599, 2012.

  • Z. Guo, T. Wittman and S. Osher, “L1 Unmixing and its Application to Hyperspectral Image Enhancement”, Proceedings of International Conference on Algorithms. and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, pp. 1181-1192, 2009.

  • J.B. Greer, “Sparse Demixing of Hyperspectral Images”, IEEE Transactions on Image Processing, Vol. 21, No. 1, pp. 219-228, 2012.

  • M.D. Iordache, J.M. Bioucas-Dias and A. Plaza, “Sparse Unmixing of Hyperspectral Data”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 2, pp. 2014-2039, 2011.

  • Y. Li, C. Yin, W. Chen and Z. Han, “Approximate Message Passing for Structured Affine Rank Minimization Problem”, IEEE Access, Vol. 5, pp. 10093-10107, 2017.

  • J. Sigurdsson, M.O. Ulfarsson and J.R. Sveinsson, “Blind Hyperspectral Unmixing using Total Variation and Lq Sparse Regularization”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 11, pp. 6371-6384, 2016.

  • C. Li, T. Sun, K. Kelly and Y. Zhang, “A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing”, IEEE Transactions on Image Processing, Vol. 21, No. 3, pp. 1200-1210, 2011.

  • M. Golbabaee, S. Arberet and P. Vandergheynst, “Compressive Source Separation: Theory and Methods for Hyperspectral Imaging”, IEEE Transactions on Image Processing, Vol. 22, No. 12, pp. 5096-5110, 2013.

  • G. Martin, J. Bioucas-Dias and A. Plaza, “HYCA: A New Technique for Hyperspectral Compressive Sensing”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 2, pp. 2819-2831, 2014.

  • Jose M. Bioucas-Dias et al., “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5, No. 2, pp. 354-379, 2012.

  • J. Fowler, “Compressive-Projection Principal Component Analysis”, IEEE Transactions on Image Processing, Vol. 18, No. 10, pp. 2230-2242, 2009.

  • J.E. Fowler and Q. Du, “Reconstructions from Compressive Random Projections of Hyperspectral Imagery”, Optical Remote Sensing, pp. 31-48, 2011.

  • C. Chen, W. Li, E. Tramel and J. Fowler, “Reconstruction of Hyperspectral Imagery from Random Projections using Multihypothesis Prediction”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 1, pp. 365-374, 2014.

  • W. Li, S. Prasad and J. Fowler, “Integration of Spectral-Spatial Information for Hyperspectral Image Reconstruction from Compressive Random Projections”, IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 6, pp. 1379-1383, 2013.

  • J.E. Candes, T. Romberg and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information”, IEEE Transactions on Information Theory, Vol. 52, No. 2, pp. 489-509, 2006.

  • C. Wu and X.C Tai, “Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models”, SIAM Journal on Imaging Sciences, Vol. 3, No. 3, pp. 300-339, 2010.

  • I.W. Selesnick and M.A.T. Figueiredo, “Signal Restoration with Overcomplete Wavelet Transforms: Comparison of Analysis and Synthesis Priors”, Proceedings of SPIE Optical Engineering, pp. 1-15, 2009.

  • Gabriel Martin and Jose. M. Bioucas-Dias, “Hyperspectral Blind Reconstruction from Random Spectral Projections”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 6, pp. 2390-2399, 2016.

  • A. Agathos, J. Li, J. M. Bioucas-Dias and A. Plaza, “Robust Minimum Volume Simplex Analysis for Hyperspectral Unmixing”, Proceedings of 22nd European Signal Processing Conference, pp. 1582-1586, 2014.

  • L.M. Bregman, “A Relaxation Method of Finding a Common Point of Convex Sets and its Application to the Solution of Problems in Convex Programming”, USSR Computational Mathematics and Mathematical Physics, Vol. 7, No. 3, pp. 200-217, 1967.

  • W Yin, S Osher, D Goldfarb and J Darbon, “Bregman Iterative Algorithms for l1 Minimization with Applications to Compressed Sensing”, SIAM Journal on Imaging Sciences, Vol. 1, No. 1, pp. 143-168, 2007.

  • Zheng Xu, Mario A.T. Figueiredo and Tom Goldstein, “Adaptive ADMM with Spectral Penalty Parameter Selection”, Proceedings of International Conference on Artificial Intelligence and Statistics, Vol. 54, pp. 718-727, 2017.

  • Hyperspectral Remote Sensing Scenes, Available at: http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes

  • J.A. Tropp and A.C. Gilbert, “Signal Recovery from Random Measurements via Orthogonal Matching Pursuit”, IEEE Transactions on Information Theory, Vol. 53, No. 12, pp. 4655-4666, 2007.

  • L. Zhang, W. Wei, Y. Zhang, C. Tian and F. Li, “Exploring Structural Sparsity by a Reweighted Laplace prior for Hyperspectral Compressive Sensing”, IEEE Transactions on Image Processing, Vol. 25, No. 10, pp. 4974-4988, 2016.

  • L. Zhang, W. Wei, Y. Zhang, C. Shen, A. Van Den Hengel and Q. Shi, “Dictionary Learning for Promoting Structured Sparsity in Hyperspectral Compressive Sensing”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 12, pp. 7223-7235, 2016.

  • Z. Wang, A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity”, IEEE Transactions on Image Processing, Vol. 13, No. 5, pp. 600-612, 2004.

  • Y. Peng, D. Meng, Z. Xu, C. Gao, Y. Yang and B. Zhang, “Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2949-2956, 2014.

  • R.H. Yuhas, J.W. Boardman and A.F.H. Goetz, “Determination of Semi-Arid Landscape Endmembers and Seasonal Trends using Convex Geometry Spectral Unmixing Techniques”, Proceedings of 4th Annual JPL Airborne Geoscience Workshop, Vol. 1, pp. 205-208, 1993.


Abstract Views: 193

PDF Views: 0




  • Compressive Sensing Approach to Hyperspectral Image Compression

Abstract Views: 193  |  PDF Views: 0

Authors

K. S. Gunasheela
Department of Electronics and Communication Engineering, NITTE Meenakshi Institute of Technology, India
H. S. Prasantha
Department of Electronics and Communication Engineering, NITTE Meenakshi Institute of Technology, India

Abstract


Hyperspectral image (HSI) processing is one of the key processes in satellite imaging applications. Hyperspectral imaging spectrometers collect huge volumes of data since the image is captured across different wavelength bands in the electromagnetic spectrum. As a result, compression of hyperspectral images is one of the active area in research community from many years. The research work proposes a new compressive sensing based approach for the compression of hyperspectral images called SHSIR (Sparsification of hyperspectral image and reconstruction). The algorithm computes the coefficients of fractional abundance map in matrix setup, which is used to reconstruct the hyperspectral image. To optimize the problem with non-smooth term existence along with large dimensionality, Bregman iterations method of multipliers is used, which converts the difficult optimization problem into simpler cyclic sequence problem. Experimental result demonstrates the supremacy of the proposed method over other existing techniques.

Keywords


Hyperspectral Image, Image Compression, Compressive Sensing.

References