Abstract
A hyperspectral image is represented as a three-dimensional tensor in this paper to realize the spatial-spectral joint compression. This avoids destroying the feature structure, as in the 2D compression model, the compression operation of the spatial and spectral information is separate. Dictionary learning algorithm is adopted to train three dictionaries on each mode and these dictionaries are applied to build the block-sparse model of hyperspectral image. Then, based on the Tucker Decomposition, the spatial and spectral information of the hyperspectral image is compressed simultaneously. Finally, the structural tensor reconstruction algorithm is utilized to recover the hyperspectral image and it significantly reduce the computational complexity in the block-sparse structure. The experimental results demonstrate that the proposed method is superior to other 3D compression models in terms of accuracy and efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chan, J.C., Ma, J.L., Van de Voorde, T., et al.: Preliminary results of superresolution - enhanced angular hyperspectral (CHRIS/Proba) images for land-cover classification. IEEE Geosci. Remote Sens. Lett. 8(6), 1011–1015 (2011)
Toivanen, P., Kubasova, O., Mielikainen, J.: Correlation-based band-ordering heuristic for lossless compression of hyperspectral sounder data. IEEE Geosci. Remote Sens. Lett. 2(1), 50–54 (2005)
Shaw, G.A., Burke, H.K.: Spectral imaging for remote sensing. Lincoln Lab. J. 14(1), 3–28 (2003)
Donoho, D.L.: Compressed sensing. IEEE Inf. Theor. 52(4), 1289–1306 (2006)
Candè, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)
Lian, Q., Shi, B., Chen, S.: Research advances on dictionary learning models, algorithms and applications. IEEE J. Autom. Sinica 41(2), 240–260 (2015)
Cesar, F.: Multidimensional compressed sensing and their applications. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(6), 355–380 (2013)
Duarte, M.F., Baraniuk, R.G.: Kronecker compressive sensing. 21(2), 494–504 (2012)
Oseledets, I.: Tensor-train decomposition. SIAM J. Sci. Comput. 33(5), 2295–2317 (2011)
Fang, L., He, N., Lin, H.: CP tensor-based compression of hyperspectral images. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 34(2), 252–258 (2017)
Töreyin, B.U., Yilmaz, O., Mert, Y.M., et al.: Lossless hyperspectral image compression using wavelet transform based spectral decorrelation. In: Recent Advances in Space Technologies, pp. 251–254. IEEE, Istanbul (2015)
Lee, C., Youn, S., Jeong, T., et al.: Hybrid compression of hyperspectral images based on PCA with pre-encoding discriminant information. IEEE Geosci. Remote Sens. Lett. 12(7), 1491–1495 (2015)
Zhao, D., Zhu, S., Wang, F.: Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding. Comput. Electr. Eng. 54, 494–505 (2016)
Karami, A., Yazdi, M., Mercier, G.: Compression of hyperspectral images using discrete wavelet transform and tucker decomposition. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 444–450 (2012)
Wang, L., Bai, J., Wu, J., et al.: Hyperspectral image compression based on lapped transform and Tucker decomposition. Signal Process Image Commun. 36, 63–69 (2015)
Yang, S., Wang, M., Li, P., et al.: Compressive hyperspectral imaging via sparse tensor and nonlinear compressed sensing. IEEE Geosci. Remote Sens. Lett. 53(11), 5043–5957 (2015)
Caiafa, C.F., Cichocki, A.: Computing sparse representations of multidimensional signals using Kronecker bases. Neural Comput. 25(1), 186–220 (2014)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (Nos. 61572372 & 41671382), LIESMARS Special Research Funding.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Chong, Y., Zheng, W., Li, H., Pan, S. (2018). Block-Sparse Tensor Based Spatial-Spectral Joint Compression of Hyperspectral Images. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-95957-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95956-6
Online ISBN: 978-3-319-95957-3
eBook Packages: Computer ScienceComputer Science (R0)