Abstract
Hyperspectral image (HSI) processing plays a very important role in satellite imaging applications. Sophisticated sensors on-board the satellite generates huge hyperspectral datasets since they capture a scene across different wavelength regions in the electromagnetic spectrum. The memory available for storage and bandwidth available to transmit data to the ground station is limited in case of satellites. As a result, compression of hyperspectral satellite images is very much necessary. The research work proposes a new algorithm called SHSIR (sparsification of hyperspectral image and reconstruction) for the compression and reconstruction of HSI acquired using compressive sensing (CS) approach. The proposed algorithm is based on the linear mixing model assumption for hyperspectral images. Compressive sensing measurements are generated by using measurement matrices containing Gaussian i.i.d. entries. HSI is reconstructed using Bregman iterations, which advance the reconstruction accuracy as well as the noise robustness. The proposed algorithm is compared with state-of-the-art compressive sensing approaches for HSI compression and the proposed algorithm performs better than existing techniques both in terms of reconstruction accuracy as well as noise robustness.
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References
Gunasheela, K.S., Prasantha, H.S.: Satellite Image Compression-Detailed Survey of the Algorithms, Proceedings of ICCR in LNNS Springer, vol. 14, pp. 187–198 (2017)
Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Tropp, J.: Just relax: convex programming methods for identifying sparse signals. IEEE Trans. Inf. Theory 51, 1030–1051 (2006)
MartÃn, G., Bioucas-Dias, J.M.: Hyperspectral blind reconstruction from random spectral projections. In: Proc. IEEE JSTARS, 2390–2399 (2016)
Agathos, A., Li, J., Bioucas-Dias, J.M., Plaza, A.: Robust minimum volume simplex analysis for hyperspectral unmixing. In: 22nd European Signal Processing Conference (EUSIPCO). Lisbon, pp. 1582–1586 (2014)
Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20, 89–97 (2004)
Xu, Z., Figueiredo, M.A.T., Goldstein, T.: Adaptive ADMM with spectral penalty parameter selection. In: International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 54, pp. 718–727, July 2017
Yin, W., Osher, S., Goldfarb, D., Darbon, J.: Bregman iterative algorithms forl1-minimization with applications to compressed sensing. SIAM J. Imaging Sci. 142–168 (2008)
Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53, 4655–4666 (2007)
Zhang, L., Wei, W., Zhang, Y., Tian, C., Li, F.: Exploring structural sparsity by a Reweighted laplace prior for hyperspectral compressive sensing. IEEE Trans. Image Process. 25, 4974–4988 (2016)
Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., Shi, Q.: Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing. IEEE Trans. Geosci. Remote Sens. 54(12), 7223–7235 (2016)
Peng, Y., Meng, D., Xu, Z., Gao, C., Yang, Y., Zhang, B.: Decomposable nonlocal tensor dictionary learning for multispectral image denoising. In: IEEE Conference on CVPR Columbus USA, pp. 2949–2956 (2014)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Yuhas, R.H., Boardman, J.W., Goetz, A.F.H.: Determination of semi-arid landscape endmembers and seasonal trends using convex geometry spectral unmixing techniques. In: Fourth Annual JPL Airborne Geosci. Workshop Washington, vol. 1. (1993)
Acknowledgements
This work is carried out as a part of Research work at Nitte Meenakshi Institute of Technology (Visvesvaraya Technological University, Belgaum). We are thankful to the institution for the kind support.
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Gunasheela, K.S., Prasantha, H.S. (2019). Compressive Sensing Approach to Satellite Hyperspectral Image Compression. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-13-1742-2_49
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DOI: https://doi.org/10.1007/978-981-13-1742-2_49
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