Compressive-sensing-based lossy compression for hyperspectral images using spectral unmixing

Wang Zhongliang; Feng Wentian; Nian Yongjian

doi:10.3788/IRLA201847.S126003

In the compressive sensing theory, the robust reconstruction of signals can be obtained from far fewer measurements than those obtained by the Nyquist theorem. Thus, it has a great potential in the onboard compression of hyperspectral images using minimal computational resources and storage memory. In this paper, a compressive-sensing-based hyperspectral image compression method was presented using spectral unmixing. At the encoder, the original image was compressed acquired by spatial sampling and spectral sampling, respectively. Then, the spectral and spatial correlation of the compressed data were studied. To improve the compression performance, spectral linear prediction was used to remove the spectral correlation, and the predictive errors were compressed by JPEG-LS in a lossless manner to generate the final bit-streams. At the decoder, the bit-streams were first decoded to obtain the sampled data. Then, a spectral unmixing technique was employed to reconstruct the original hyperspectral image, which can avoid the defect of conventional compressed sensing reconstruction. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor show that the proposed algorithm provides better compression performance than JPEG2000 and DCT-JPEG2000 with a lower computational complexity.

Compressive-sensing-based lossy compression for hyperspectral images using spectral unmixing

1. Department of Electric Engineering,Tongling University,Tongling 244061,China;

2. Branch 72 of No. 32142 Army,Baoding 071000,China;

3. School of Biomedical Engineering and Imaging Medicine,Army Medical University(Third Military Medical University),Chongqing 400038,China

Received Date: 2018-03-23

Rev Recd Date: 2018-05-19

Publish Date: 2018-06-25

Key words:

Abstract: In the compressive sensing theory, the robust reconstruction of signals can be obtained from far fewer measurements than those obtained by the Nyquist theorem. Thus, it has a great potential in the onboard compression of hyperspectral images using minimal computational resources and storage memory. In this paper, a compressive-sensing-based hyperspectral image compression method was presented using spectral unmixing. At the encoder, the original image was compressed acquired by spatial sampling and spectral sampling, respectively. Then, the spectral and spatial correlation of the compressed data were studied. To improve the compression performance, spectral linear prediction was used to remove the spectral correlation, and the predictive errors were compressed by JPEG-LS in a lossless manner to generate the final bit-streams. At the decoder, the bit-streams were first decoded to obtain the sampled data. Then, a spectral unmixing technique was employed to reconstruct the original hyperspectral image, which can avoid the defect of conventional compressed sensing reconstruction. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor show that the proposed algorithm provides better compression performance than JPEG2000 and DCT-JPEG2000 with a lower computational complexity.

HTML

Reference (14)

[1]	Penna B, Tillo T, Magli E, et al. Transform coding techniques for lossy hyperspectral data compression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5):1408-1421.
[2]	Blanes I, Serra-Sagrista J. Pairwise orthogonal transform for spectral image coding[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3):961-972.
[3]	Nian Y J, Liu Y, Ye Z. Pairwise KLT-based compression for multispectral images[J]. Sensing and Imaging, 2016, 17(1):1-15.
[4]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[5]	Fowler J E. Compressive-projection principal component analysis[J]. IEEE Transactions on Image Processing, 2009, 18(10):2230-2242.
[6]	Jia Y B, Feng Y, Wang Z L. Reconstructing hyperspectral images from compressive sensors via exploiting multiple priors[J]. Spectroscopy Letters, 2015, 48(1):22-26.
[7]	Chen C, Li W, Tramel E W, et al. Reconstruction of hyperspectral imagery from random projections using multihypothesis prediction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1):365-374.
[8]	Li C B, Sun T, Kelly K F, et al. A compressive sensing and unmixing scheme for hyperspectral data processing[J]. IEEE Transactions on Image Processing, 2012, 21(3):1200-1210.
[9]	Zhang L, Wei W, Zhang Y, et al. Locally similar sparsity-based hyperspectral compressive sensing using unmixing[J]. IEEE Transactions on Computational Imaging, 2016, 2(2):86-100.
[10]	Zhang L, Wei W, Tian C N. Exploring structured sparsity by a reweighted laplace prior for hyperspectral compressive sensing[J]. IEEE Transactions on Image Processing, 2016, 25(10):4974-4988.
[11]	Wang Z L, Feng Y, Jia Y B. Spatio-spectral hybrid compressive sensing of hyperspectral imagery[J]. Remote Sensing Letters, 2015, 6(3):199-208.
[12]	Huo C F, Zhang R, Yin D. Compression technique for compressed sensing hyperspectral images[J]. International Journal of Remote Sensing, 2012, 33(5):1586-1604.
[13]	Dias J M B, Nascimento J M P. Hyperspectral subspace identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8):2435-2445.
[14]	Nascimento J M P, Dias J M B. Vertex component analysis:a fast algorithm to unmix hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4):898-910.

Compressive-sensing-based lossy compression for hyperspectral images using spectral unmixing

doi: 10.3788/IRLA201847.S126003

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views