Abstract
This paper presents an approach for hyperspectral image classification using contextual sparse coefficients based on sparse representations. The main idea is that the sparse coefficients obtained through sparse representation-based modelling of the hyperspectral images contain discriminative characters which can be utilized for hyperspectral image processing tasks, such as classifications. Moreover, such discriminative features can be enhanced by incorporating the contextual information in the sparse coefficient domain. The proposed method starts with finding a representative spectral dictionary using the training data. Sparse coding is then applied to obtain the sparse coefficients. This is followed by a contextual transform performed in the sparse coefficient domain to incorporate the spatial information. Finally, the contextual sparse coefficients are used as feature vectors for training traditional classifiers, namely SVMs and kNN in our case. The experimental results conducted on a number of hyperspectral data sets confirm the effectiveness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The data sets are freely available at: http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes.
References
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Proc. 54(11), 4311–4322 (2006)
Awate, S., Koushik, N.: Robust dictionary learning on the Hilbert sphere in kernel feature space. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016, vol. 9851, pp. 731–748. Springer, Cham (2016)
Bertsekas, D.P.: Nonlinear Programming, 2nd edn. Athena Scientific, Belmont (1999)
Blanzieri, E., Melgani, F.: Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans. Geosci. Remote Sens. 46(6), 1804–1811 (2008)
Camps-Valls, G., Gomez-Chova, L., Muñoz-Marí, J., Vila-Francís, J., Calpe-Maravilla, J.: Composite kernels for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)
Charles, A., Olshausen, B., Rozell, C.: Learning sparse codes for hyperspectral imagery. IEEE J. Sel. Top. Signal Process. 5(5), 963–978 (2011)
Chen, S., Donoho, D., Saunders, M.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)
Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973–3985 (2011)
Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximation. Constr. Approx. 13, 57–98 (1997)
Engan, K., Aase, S., Husøy, J.: Method of optimal directions for frame design. In: Proceedings of the ICASSP ’99 (Phoenix, USA), pp. 2443–2446 (1999)
Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.: Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans. Geosci. Remote Sens. 46(11), 3804–3814 (2008)
Goetz, A.F.H.: Three decades of hyperspectral remote sensing of the earth: a personal view. Remote Sens. Environ. 113, S5–S16 (2009)
Green, R.O., Eastwood, M.L., Sarture, C.M., Chrien, T.G., Aronsson, M., Chippendale, B.J., Faust, J.A., Pavri, B.E., Chovit, C.J., Solis, M., Olah, M.R., Williams, O.: Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 65(3), 227–248 (1998)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: 26th International Conference on Machine Learning, Montreal, Canada (2009)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)
Mallat, S., Zhang, Z.: Matching pursuit with time-frequency dictionaries. IEEE Trans. Signal Proc. 41(12), 3397–3415 (1993)
Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004)
Mercier, G., Lenon, M.: Support vector machines for hyperspectral image classification with spectral-based kernels. In: 2003 IEEE InternationalGeoscience and Remote Sensing Symposium, IGARSS 2003, vol 1, pp. 288–290 (2003)
Olshausen, B., Field, D.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res. 37(23), 3311–3325 (1997)
Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J.C., Trianni, G.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, 110–122 (2009)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Soltani-Farani, A., Rabiee, H.R., Hosseini, S.A.: Spatial-aware dictionary learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 53(1), 527–541 (2013)
Tropp, J.A., Gilbert, A.C., Strauss, M.J.: Algorithms for simultaneous sparse approximation: Part I: greedy pursuit. Signal Process. 86(3), 572–588 (2006)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, H., Celik, T. Sparse representation-based hyperspectral image classification. SIViP 12, 1009–1017 (2018). https://doi.org/10.1007/s11760-018-1249-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-018-1249-1