طبقه بندی تصاویر ابرطیفی با استفاده از نمایش رتبه پایین و اطلاعات طیفی - مکانی
محورهای موضوعی : مهندسی الکترونیک
فاطمه حاجیانی
1
(گروه برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران)
ناصر پرهیزگار
2
(گروه برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران)
احمد کشاورز
3
(گروه مهندسی برق، دانشکده مهندسی سیستم های هوشمند و علوم داده، دانشگاه خلیج فارس بوشهر، بوشهر، ایران)
کلید واژه: نمایش رتبه پایین, طبقه بندی, نمایش تنک, تصویر ابرطیفی,
چکیده مقاله :
طبقه بندی تصاویر ابرطیفی از مهمترین پردازش هایی است که بر روی این تصاویر انجام می شود. تصاویر ابرطیفی دارای ابعاد بالایی هستند و این امر طبقه بندی این تصاویر را با مشکل مواجه کرده است. از این رو روش هایی که ساختار زیرفضا با بعد پایین را از تصویر ابرطیفی استخراج کنند مورد توجه قرار می گیرند. روش نمایش رتبه پایین می تواند ساختار زیر فضا با بعد پایین را که در داده ها وجود دارد استخراج کند. این روش ساختار سراسری داده ها را در نظر می گیرد. به منظور حفظ ساختار سراسری و محلی در داده ها دراین مقاله روش استخراج ویژگی نمایش رتبه پایین و تنک بر مبنای اطلاعات طیفی و مکانی ارائه شده است. با اعمال این مدل ساختار داده بهتر آشکار می شود و قدرت تمایز ویژگی های آن افزایش می یابد. در این مدل هر پیکسل به صورت ترکیب خطی از مولفه های دیکشنری بیان می شود. بعلاوه برای حل مسئله به صورت بهینه از روش جهتی متناوب مضارب استفاده شده است. نتایج شبیه سازی نشان می دهد که مدل پیشنهادی نتایج بهتری را نسبت به روش های دیگر بدست آورده است.
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.