Hyperspectral Image Classification Using Low-Rank Representation and Spectral-Spatial Information
Subject Areas : Electronics Engineering
Fatemeh Hajiani
1
,
Naser Parhizgar
2
,
Ahmad Keshavarz
3
1 - Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 - Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
3 - IoT and Signal Processing Research Group, ICT Research Institute, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, 7516913817 Bushehr, Iran
Keywords: sparse representation, Classification, Hyperspectral image, Low-rank representation,
Abstract :
Classification of hyperspectral images is one of the most important processes on these images. Hyperspectral images are high dimensional, so classification of these images is difficult. Therefore, methods that extract low-dimensional subspace structures from the hyperspectral image are considered. The low-rank representation method can extract the low-dimensional subspace structure in the data. This method considers the global structure of the data. In this paper, to preserve the global and local structure in the data, spares and low-rank representation feature extraction method based on spectral and spatial information is presented. The data structure is better revealed using this model, and the discrimination of the features is increased. In this model, each pixel is expressed by a linear combination of dictionary atoms. In addition, to solve the optimization problem, the alternating direction method of multipliers has been used. The simulation results show that the proposed model has better results than other methods.
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_||_[1] F. A. Kruse, J. W. Boardman, and J. F. Huntington, "Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping," IEEE Trans. Geosci. Remote Sens., vol. 41,no.6, pp. 1388-1400, 2003, doi: 10.1109/tgrs.2003.812908.
[2] N. C. Shirazi, R. Hamzehyan, and A. Masoomi, " The Comparison of Classification Algorithms for Remote Sensing Images," Journal of Communication Engineering., vol. 5,no.17, pp. 31-38, 2015(in persian).
[3] M. Hamed, F. Hajiani, " A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering," Journal of Communication Engineering., vol. 10,no.37, pp. 65-72, 2020(in persian).
[4] G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. 14,no.1, pp. 55-63, 1968, doi: 10.1109/TIT.1968.1054102.
[5] G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 3,no.1, pp. 93-97, 2006, doi: 10.1109/LGRS.2005.857031
[6] Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. Geosci. Remote Sens., vol.51,no.1, pp.217-23, 2013, doi: 10.1109/TGRS.2012.2201730.
[7] Q. S. Ul Haq, L. Tao, F. Sun, and S. Yang, "A fast and robust sparse approach for hyperspectral data classification using a few labeled samples," IEEE Trans. Geosci. Remote Sens., vol. 50,no.6, pp. 2287-2302, 2012, doi: 10.1109/TGRS.2011.2172617.
[8] A. Rakotomamonjy, "Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms," Signal Process., vol. 91,no.7, pp. 1505-1526, 2011, doi: 10.1016/j.sigpro.2011.01.012.
[9] Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Transactions on Geoscience and Remote sensing, vol. 51,no.1, pp. 217-231, 2013, doi: 10.1109/TGRS.2012.2201730.
[10] L. Pan, H.-C. Li, H. Meng, W. Li, Q. Du, and W. J. Emery, "Hyperspectral image classification via low-rank and sparse representation with spectral consistency constraint," IEEE Geosci. Remote Sens. Lett., vol. 14,no.11, pp. 2117-2121, 2017, doi: 10.1109/LGRS.2017.2753401.
[11] G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma, "Robust recovery of subspace structures by low-rank representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35,no.1, pp. 171-184, 2013, doi: 10.1109/TPAMI.2012.88.
[12] S. G. Mallat and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. Signal Process., vol. 41,no.12, pp. 3397-3415, 1993, doi: 10.1109/78.258082.
[13] R. Gribonval, "Fast matching pursuit with a multiscale dictionary of Gaussian chirps," IEEE Trans. Signal Process., vol. 49,no.5, pp. 994-1001, 2001, doi: 10.1109/78.917803.
[14] S. Fischer, G. Cristóbal, and R. Redondo, "Sparse overcomplete Gabor wavelet representation based on local competitions," IEEE Trans. Image Process., vol. 15,no.2, pp. 265-272, 2006, doi: 10.1109/TIP.2005.860614.
[15] K. Engan, S. O. Aase, and J. H. Husoy, "Method of optimal directions for frame design," in IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999, pp. 2443-2446, doi: 10.1109/ICASSP.1999.760624.
[16] M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54,no.11, pp. 4311-4322, 2006.doi: 10.1109/TSP.2006.881199.
[17] A. Soltani-Farani, H. R. Rabiee, and S. A. Hosseini, "Spatial-aware dictionary learning for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 53,no.1, pp. 527-541, 2015, doi: 10.1109/TGRS.2014.2325067.
[18] Z. He, L. Liu, R. Deng, and Y. Shen, "Low-rank group inspired dictionary learning for hyperspectral image classification," Signal Process., vol. 120, pp. 209-221, 2016.doi: 10.1016/j.sigpro.2015.09.004.
[19] M. V. Afonso, J. M. Bioucas-Dias, and M. A. Figueiredo, "An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems," IEEE Trans. Image Process., vol. 20,no.3, pp. 681-695, 2011.doi: 10.1109/TIP.2010.2076294.
[20] L. Mirsky, An introduction to linear algebra: Courier Corporation, 2012.
[21] C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol, vol. 2, pp. 1-27, 2011.
[22] Y. Xiao, H. Wang, and W. Xu, "Parameter selection of Gaussian kernel for one-class SVM," IEEE Trans. Cybern., vol. 45,no.5, pp. 941-953, 2015, doi: 10.1109/TCYB.2014.2340433.
[23] M. Cui and S. Prasad, "Class-dependent sparse representation classifier for robust hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 53,no.5, pp. 2683-2695, 2015, doi: 10.1109/TGRS.2014.2363582.
[24] C. Li, Y. Ma, X. Mei, C. Liu, and J. Ma, "Hyperspectral image classification with robust sparse representation," IEEE Geosci. Remote Sens. Lett., vol. 13,no.5, pp. 641-645, 2016, doi: 10.1109/LGRS.2016.2532380.
[25] G. Liu, Z. Lin, and Y. Yu, "Robust subspace segmentation by low-rank representation," in Proceedings of the 27th International Conference on International Conference on Machine Learning, 2010, pp.663-670.
[26] M. Graña, M. A. Veganzons, and B. Ayerdi, "Hyperspectral remote sensing scenes," ed. Accessed Jun 1, 2018 . http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes, 2018.
