Hyperspectral Image Compression and Reconstruction Based on Block-Sparse Dictionary Learning

A large amount of hyperspectral image (HSI) data poses a significant challenge for transmission and storage. A new signal processing mechanism—compressed sensing (CS)—is appropriate for processing signals with a massive amount of data and can achieve high reconstruction accuracy. According to the structural properties of HSI, the same ground features show the same spectral properties. In this paper, an approach is proposed to compress and reconstruct HSI based on CS and block-sparse dictionary learning. Primarily, a dictionary of a given set of signal is trained and prior knowledge is not required on the association of the training dataset into groups. Then, a measurement matrix is used to compress an HSI cube to reduce the data volume of the signal. Finally, we use the trained block-sparse dictionary to reconstruct the image, along with the HSI feature classification information. Our experimental results showed that, for block-sparse HSI data, the proposed approach significantly improved the performance compared with other related state of the art methods.

[1]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[2]  J. Mielikainen,et al.  Lossless compression of hyperspectral images using lookup tables , 2006, IEEE Signal Processing Letters.

[3]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[4]  Mark Tygert,et al.  A Randomized Algorithm for Principal Component Analysis , 2008, SIAM J. Matrix Anal. Appl..

[5]  Rémi Gribonval,et al.  Learning unions of orthonormal bases with thresholded singular value decomposition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[6]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[7]  V. S. Saroja,et al.  A survey on compressive sensing , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[8]  Yan Feng,et al.  Spatial-spectral compressive sensing of hyperspectral image , 2013, 2013 IEEE Third International Conference on Information Science and Technology (ICIST).

[9]  Ian Blanes,et al.  Cost and Scalability Improvements to the Karhunen–Loêve Transform for Remote-Sensing Image Coding , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Qian Du,et al.  Segmented Principal Component Analysis for Parallel Compression of Hyperspectral Imagery , 2009, IEEE Geoscience and Remote Sensing Letters.

[11]  Yonina C. Eldar,et al.  Dictionary Optimization for Block-Sparse Representations , 2010, IEEE Transactions on Signal Processing.

[12]  Wei Wei,et al.  Reweighted laplace prior based hyperspectral compressive sensing for unknown sparsity , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Jianglin Ma,et al.  Preliminary Results of Superresolution-Enhanced Angular Hyperspectral (CHRIS/Proba) Images for Land-Cover Classification , 2011, IEEE Geoscience and Remote Sensing Letters.

[14]  Qian Du,et al.  Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis , 2007, IEEE Geoscience and Remote Sensing Letters.

[15]  B. Penna,et al.  A New Low Complexity KLT for Lossy Hyperspectral Data Compression , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[16]  Mike E. Davies,et al.  Compressible dictionary learning for fast sparse approximations , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

[17]  Andreas Spanias,et al.  Multilevel dictionary learning for sparse representation of images , 2011, 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE).

[18]  Yihua Tan,et al.  Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

[19]  Michael Elad,et al.  Multi-Scale Dictionary Learning Using Wavelets , 2011, IEEE Journal of Selected Topics in Signal Processing.

[20]  Lian Qiu,et al.  Research Advances on Dictionary Learning Models, Algorithms and Applications , 2015 .

[21]  Jin Jiang,et al.  Analysis in Theory and Technology Application of Compressive Sensing , 2014, 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics.

[22]  Xu Ke,et al.  Block compressive sensing of hyperspectral images based on prediction error , 2015, 2015 4th International Conference on Computer Science and Network Technology (ICCSNT).

[23]  Michael Elad,et al.  Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.

[24]  Gary A. Shaw,et al.  Spectral Imaging for Remote Sensing , 2003 .

[25]  Yonina C. Eldar,et al.  Structured Compressed Sensing: From Theory to Applications , 2011, IEEE Transactions on Signal Processing.

[26]  Abdelhak M. Zoubir,et al.  Compressive sensing and adaptive direct sampling in hyperspectral imaging , 2014, Digit. Signal Process..

[27]  Peng Li,et al.  Compressive Hyperspectral Imaging via Sparse Tensor and Nonlinear Compressed Sensing , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[28]  William A. Pearlman,et al.  Successive Approximation Wavelet Coding of AVIRIS Hyperspectral Images , 2011, IEEE Journal of Selected Topics in Signal Processing.

[29]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[30]  Enrico Magli,et al.  Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC , 2004, IEEE Geoscience and Remote Sensing Letters.

[31]  William L. Smith,et al.  Hyperspectral remote sensing of atmospheric profiles from satellites and aircraft , 2001, SPIE Asia-Pacific Remote Sensing.

[32]  Julien Mairal,et al.  Proximal Methods for Hierarchical Sparse Coding , 2010, J. Mach. Learn. Res..

[33]  Barnabás Póczos,et al.  Online group-structured dictionary learning , 2011, CVPR 2011.

[34]  Jarno Mielikäinen,et al.  Correlation-based band-ordering heuristic for lossless compression of hyperspectral sounder data , 2005, IEEE Geoscience and Remote Sensing Letters.

[35]  Jarno Mielikäinen,et al.  Clustered DPCM for the lossless compression of hyperspectral images , 2003, IEEE Trans. Geosci. Remote. Sens..

[36]  Ajit Rajwade,et al.  Block and Group Regularized Sparse Modeling for Dictionary Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.