Train Support Vector Machine Using Fuzzy C-means Without a Prior Knowledge for Hyperspectral Image Content Classification
Abstract
In this paper, a new cooperative classification method called auto-train support vector machine (SVM) is proposed. This new method converts indirectly SVM to an unsupervised classification method. The main disadvantage of conventional SVM is that it needs a priori knowledge about the data to train it. To avoid using this knowledge that is strictly required to train SVM, in this cooperative method, the data, that is, hyperspectral images (HSIs), are first clustered using Fuzzy C-means (FCM); then, the created labels are used to train SVM. At this stage, the image content is classified using the auto-trained SVM. Using FCM, clustering reveals how strongly a pixel is assigned to a class thanks to the fuzzification process. This information leads to gaining two advantages, the first one is that no prior knowledge about the data (known labels) is needed and the second one is that the training data selection is not done randomly (the training data are selected according to their degree of membership to a class). The proposed method gives very promising results. The method is tested on two HSIs, which are Indian Pines and Pavia University. The results obtained have a very high accuracy of the classification and exceed the existing manually trained methods in the literature.
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Akbari, H., Kosugi, Y., Kojima, K. and Tanaka, N., 2010. Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging. IEEE Transactions on Biomedical Engineering, 57, pp.2011-2017.
Alameddine, J., Chehdi, K. and Cariou, C., 2021. Hierarchical unsupervised partitioning of large size data and its application to hyperspectral images. Remote Sensing, 13, p.4874.
Awad, M. and Khanna, R., 2015. Support Vector Machines for Classification. Springer, Berlin. pp.39-66.
Bezdek, J.C., Ehrlich, R. and Full, W., 1984. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10, pp.191-203.
Boser, B.E., Guyon, I.M. and Vapnik, V.N., 1992. Atraining algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual Workshop on Computational Learning Theory, COLT’ 92. Association for Computing Machinery, New York, USA. pp.144-152.
Bruzzone, L. and Cossu, R., 2002. A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps. IEEE Transactions on Geoscience and Remote Sensing, 40, pp.1984-1996.
Camps-Valls, G. and Bruzzone, L., 2005. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43, pp.1351-1362.
Cariou, C., Le Moan, S. and Chehdi, K., 2022. Anovel mean-shift algorithm for data clustering. IEEE Access, 10, pp.14575-14585.
Cariou, C., Moan, S. and Chehdi, K., 2020. Improving K-nearest neighbor approaches for density-based pixel clustering in hyperspectral remote sensing images. Remote Sensing, 12, p.3745.
Chen, Y., Nasrabadi, N.M. and Tran, T.D., 2013. Hyperspectral image classification via kernel sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 51, pp.217-231.
Cristianini, N. and Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge.
Dong, Y., Liu, Q., Du, B. and Zhang, L., 2022. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification. IEEE Transactions on Image Processing, 31, pp.1559-1572.
Duda, R.O. and Hart, P.E., 1973. Pattern Classification and Scene Analysis. 1st ed. Wiley, New York.
Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J. and Tilton, J.C., 2013. Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 101, pp.652-675.
Feng, Y.Z. and Sun, D.W., 2012. Application of hyperspectral imaging in food safety inspection and control: A review. Critical Reviews in Food Science and Nutrition, 52, pp.1039-1058.
Goetz, A.F.H., Vane, G., Solomon, J.E. and Rock, B.N., 1985. Imaging spectrometry for earth remote sensing. Science, 228, pp.1147-1153.
Gove, R. and Faytong, J., 2012. Chapter 4 machine learning and event-based software testing: Classifiers for identifying infeasible Gui event sequences. In: Hurson, A. and Memon, A. Eds. Advances in Computers. Elsevier, Amsterdam, Netherlands. pp.109-135.
Guo, Y., Yin, X., Zhao, X., Yang, D. and Bai, Y., 2019. Hyperspectral image classification with SVM and guided filter. EURASIP Journal on Wireless Communications and Networking, 2019, p.56.
Hughes, G., 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14, pp.55-63.
Lacar, F.M., Lewis, M.M. and Grierson, I.T., 2001. Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia. In: Geoscience and Remote Sensing Symposium, 2001. IGARSS ’01. IEEE 2001 International. pp.2875-2877.
Lagueux, P., Puckrin, E., Turcotte, C.S., Gagnon, M.A., Bastedo, J., Farley, V. and Chamberland, M., 2012. Airborne Infrared Hyperspectral Imager for Intelligence, Surveillance and Reconnaissance Applications. Proceedings of SPIE the International Society for Optical Engineering.
Li, X., Lim, B.S., Zhou, J.H., Huang, S., Phua, S.J., Shaw, K.C. and Er, M.J., 2009. Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation. Annual Conference of the PHM Society. Available from: http://www.papers.phmsociety.org/index.php/phmconf/article/view/1403 [Last accessed 2022 Jul 02].
Li, Y., Li, J. and Pan, J.S., 2019. Hyperspectral image recognition using SVM combined deep learning. Journal of Internet Technology, 20, pp.851-859.
MacQueen, J., 1967. Some Methods for Classification and Analysis of Multivariate Observations. Vol. 5. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability.
Mounika, K., Aravind, K., Yamini, M., Navyasri, P., Dash, S. and Suryanarayana, V., 2021. Hyperspectral Image Classification Using SVM with PCA. In: 2021 6th International Conference on Signal Processing, Computing and Control. pp.470-475.
Pathak, D.K. and Kalita, S.K., 2019. Spectral Spatial Feature Based Classification of Hyperspectral Image Using Support Vector Machine. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, Noida, India. pp.430-435.
Ren, J., Wang, R., Liu, G., Wang, Y. and Wu, W., 2021. An SVM-based nested sliding window approach for spectral spatial classification of hyperspectral images. Remote Sensing, 13, p.114.
Ryan, J.P., Davis, C.O., Tufillaro, N.B., Kudela, R.M. and Gao, B.C., 2014. Application of the hyperspectral imager for the coastal ocean to phytoplankton ecology studies in Monterey Bay, CA, USA. Remote Sens, 6, pp.1007-1025.
Sellami, A. and Tabbone, S., 2022. Deep neural networks-based relevant latent representation learning for hyperspectral image classification. Pattern Recognition, 121, p.108224.
Shang, Y., Zheng, X., Li, J., Liu, D. and Wang, P., 2022. A comparative analysis of swarm intelligence and evolutionary algorithms for feature selection in SVMbased hyperspectral image classification. Remote Sensing, 14, p.3019.
Sun, L., Zhao, G., Zheng, Y. and Wu, Z., 2022. Spectral spatial feature tokenization transformer for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1-14.
Tarabalka, Y., Fauvel, M., Chanussot, J. and Benediktsson, J.A., 2010. SVM and MRF-based method for accurate classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 7, pp.736-740.
Tarabalka, Y., Benediktsson, J. and Chanussot, J., 2009. Classification of Hyperspectral Data Using Support Vector Machines and Adaptive Neighborhoods. International Conference on Image Processing.
Tarabalka, Y., Chanussot, J., Benediktsson, J., Angulo, J. and Fauvel, M., 2008. Segmentation and Classification of Hyperspectral Data Using Watershed. IGARSS 2020 Proceedings, pp.652-655.
Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer, New York.
Wu, Y., Yang, X., Plaza, A., Qiao, F., Gao, L. and Cui, Y., 2016. Approximate computing of remotely sensed data: SVM hyperspectral image classification as a case study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, pp.1-13.
Zhao, C., Zhao, H., Wang, G. and Chen, H., 2020. Improvement SVM classification performance of hyperspectral image using chaotic sequences in artificial bee colony. IEEE Access, 8, pp.73947-73956.
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