Published Jun 15, 2017


Google Scholar
Search GoogleScholar

David Alberto Boada-supelano, BSc

Héctor Miguel Vargas-Garcia, BSc

Jaime Octavio Albarracín-Ferreira, PhD

Henry Arguello-Fuentes, PhD



Hyperspectral imaging requires handling a large amount of multidimensional spectral information. Hyperspectral image acquisition, processing, and storage are computationally and economically expensive and, in most cases, slow processes. In recent years, optical architectures have been developed for acquisition of spectral information in compressed form by using a small set of measurements coded by a spatial modulator. This article formulates a processing scheme that allows the measurements acquired by such compressive sampling systems to be used to perform spectral detection of targets, by adapting traditional detection algorithms for use in the compressive sampling model, and shows that the performance is comparable with that obtained by detection processes without compression.


Compressive sensing, Hyperspectral target detection, Hyperspectral imaging, Sparsity modelimágenes hiperespectrales, muestreo compresivo, detección de objetivos, modelo de escasez

[1] J.-S. Pan, P.-W. Tsai, and H.-C. Huang, Advances in Intelligent Information Hiding and Multimedia Signal Processing. New York: Springer, 2016. doi: 10.1007/978-3-319-50209-0
[2] R. Singh, M. Vatsa, A. Majumdar, and A. Kumar, eds., Machine Intelligence and Signal Processing. New York: Springer, 2016. doi: 10.1007/978-81-322-2625-3
[3] G. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Trans. Inf. Theory, vol. 14, no. 1, pp. 55-63, 1968.
[4] 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 Trans. Geosci. Remote Sens., vol. PP, no. 99, pp. 7223–7235, 2016.
[5] A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev., vol. 51, no. 1, pp. 34–81, 2009.
[6] M. F. Duarte and Y. C. Eldar, “Structured compressed sensing: From theory to applications,” IEEE Trans. Signal Process., vol. 59, no. 9, 2011.
[7] M. Ying Yang, S. Feng, H. Ackermann, and B. Rosenhahn, “Global and local sparse subspace optimization for motion segmentation,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. II-3/W5, pp. 475–482, 2015.
[8] Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang, “Deep networks for image super-resolution with sparse prior,” Proc. IEEE Int. Conf. Comput. Vis., vol. 11–18–Dece, pp. 370–378, 2016.
[9] W. Dong, G. Shi, Y. Ma, and X. Li, “Image restoration via simultaneous sparse coding: Where structured sparsity meets Gaussian scale mixture,” Int. J. Comput. Vis., pp. 1–16, 2015.
[10] X. Jiang and J. Lai, “Sparse and dense hybrid representation via dictionary decomposition for face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 5, pp. 1067–1079, 2015.
[11] J. K. Pillai, V. Patel, R. Chellappa, and N. Ratha, eds., “Robust and secure iris recognition,” in Handbook of Iris Recognition. New York: Springer 2016, pp. 247-268. doi: 10.1007/978-1-4471-6784-6
[12] S. D. S. Al-Shaikhli, M. Y. Yang, and B. Rosenhahn, “Brain tumor classification and segmentation using sparse coding and dictionary learning,” Biomed. Tech. (Berl.), vol. 61, no. 4, 2016, pp. 413-429. [Online]. doi: 10.1515/bmt-2015-0071
[13] G. Camps-Valls, T. V Bandos Marsheva, and D. Zhou, “Semi-supervised graph-based hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp.3044–3054, 2007.
[14] L. Fang, S. Li, X. Kang, and S. Member, “Spectral – spatial hyperspectral image classification via multiscale adaptive sparse representation,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7738–7749, 2014.
[15] W. Li, S. Prasad, and J. E. Fowler, “Classification and reconstruction from random projections for hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 833–843, 2013.
[16] H. Arguello and G. R. Arce, “Colored coded aperture design by concentration of measure in compressive spectral imaging,” IEEE Trans. Image Process., vol. 23, no. 4, pp. 1896–1908, 2014.
[17] G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and David S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag., vol. 31, no. 1, pp. 105–115, 2014. [Online]. doi: 10.1109/MSP.2013.2278763
[18] H. Arguello and G. R. Arce, “Code aperture optimization for spectrally agile compressive imaging,” J. Opt. Soc. Am. A, vol. 28, no. 11, p. 2400, 2011. [19] A. Ramirez, S. Member, H. Arguello, S. Member, G. R. Arce, and B. M. Sadler, “Spectral image classification from optimal coded-aperture compressive measurements,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 6, pp. 3299–3309, 2014.
[20] X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graph., vol. 33, no. 6, 2014. [Online]. doi: 10.1145/2661229.2661262 [21] A. Ramirez, G. R. Arce, and B. M. Sadler, “Spectral image unmixing from optimal measurements,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 405–415, 2015.
[22] H. Rueda, H. Arguello, and G. R. Arce, “DMD-based implementation of patterned optical filter arrays for compressive spectral imaging,” J. Opt. Soc. Am. A, vol. 32, no. 1, p. 80, 2014.
[23] Y. Chen, N. M. Nasrabadi, and T. D. Tran, “Simultaneous joint sparsity model for target detection in hyperspectral imagery,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 4, pp. 676–680, 2011.
[24] Y. Chen, N. M. Nasrabadi, and T. D. Tran, “Sparse representation for target detection in hyperspectral imagery,” Sel. Top. Signal Process. IEEE J., vol. 5, no. 3, pp. 629–640, 2011.
[25] S. Kim, “An interior-point method for large-scale logistic regression,” J. Mach. Learn. Res., vol. 8, pp. 1519–1555, 2007.
[26] M. Figueiredo, R. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process., vol, no. 1, pp. 586-597, 2007.
[27] D. Snyder, J. Kerekes, I. Fairweather, R. Crabtree, J. Shive, and S. Hager, “Development of a web-based application to evaluate target finding algorithms,” Int. Geosci. Remote Sens. Symp., vol. 2, no. 1, pp. 915–918, 2008.
How to Cite
Boada-supelano, D. A., Vargas-Garcia, H. M., Albarracín-Ferreira, J. O., & Arguello-Fuentes, H. (2017). A sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture. Ingenieria Y Universidad, 21(2), 273–288.
Electrical and computer engineering