Abstract
The paper propounds a concept of incorporating composite kernel methods with fuzzy-based image classifiers. The study incorporates noise classifier as a fuzzy classifier. The work demonstrates how nonlinearity among the different classes of remote sensing data with uncertainty is handled with noise classifier without entropy (fuzzy classifier) using composite kernel technique for land use/land cover maps generation. It also showcases the comparative study between the performance of Noise Classifier with Euclidean Distance and Noise Classifier with Composite Kernel functions. This study has incorporated the composition of two prominent kernels: Spectral and KMOD Kernel. The performance of both the classifier is evaluated in supervised mode and, image-to-image assessment of accuracy has been carried out using FERM (Fuzzy Error Matrix).
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References
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Upadhyay, P., Ghosh, S.K., Kumar, A: A brief review of fuzzy soft classification and assessment of accuracy methods for identification of single land cover. Stud. Surv. Mapp. Sci. (SSMS), 2(Mlc), 1–13 (2014)
Davé, R., Sen, S.: Noise clustering algorithm revisited. In: Annual Meeting of the North American Fuzzy Information Processing Society, 1997. NAFIPS’97, pp. 199–204. IEEE, New York (1997)
Chotiwattana, W.: Noise clustering algorithm based on kernel method. In: Advance Computing Conference. IACC 2009, pp. 56–60. IEEE, New York (2009)
Ayat, N.E., Cheriet, M., Remaki, L., Suen, C.Y: KMOD-a new support vector machine kernel with moderate decreasing. In: Proceedings of the Sixth International Conference on Document Analysis and Recognition, pp. 1215–1219 (2001)
Gu, Y., Jocelyn, C., Jia, X., Benediktsson, J.A.: Multiple kernel learning for hyperspectral image classification: a review. IEEE Trans. Geosci. Remote. Sens. (2017)
Vidnerova, P., Neruda, R.: Evolving sum and composite kernel functions for regularization networks. In: Adaptive and Natural Computing Algorithms, pp. 180–189 (2011)
Byju, A.P.: Non-Linear Separation of classes using a Kernel based Fuzzy c-Means (KFCM) Approach. M.Sc. Thesis, ITC, University of Twente, The Netherlands (2015)
Camps-Valls, G., Gomez-Chova, L., Mñnoz-MarÃ, J., Vila-Francés, J., Calpe-Maravilla, J.: Composite kernels for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. (2005)
Mittal, D., Tripathy, B.K: Efficiency analysis of kernel functions in uncertainty based C-means algorithms. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 807–813 (2015)
Li, W., Ong, K.L., Ng, W.K., Sun, A.: Spectral kernels for classification. In: International Conference on Data Warehousing and Knowledge Discovery, DAWAK 2005, pp. 520–529 (2015)
Landsat-8 Specifications. https://landsat.usgs.gov/landsat-8
Formosat-2 Specification. https://earth.esa.int/web/eoportal/satellite-missions/f/formosat-2
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SenGupta, I., Kumar, A., Dwivedi, R.K. (2019). Assessment of Spectral-KMOD Composite Kernel-Based Supervised Noise Clustering Approach in Handling Nonlinear Separation of Classes. In: Hu, YC., Tiwari, S., Mishra, K., Trivedi, M. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 904. Springer, Singapore. https://doi.org/10.1007/978-981-13-5934-7_40
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DOI: https://doi.org/10.1007/978-981-13-5934-7_40
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