Abstract
In this paper, an adaptive differential evolution fuzzy clustering algorithm with spatial information and kernel metric for remote sensing imagery, namely KADESFC, is proposed. In KADESFC, the clustering problem is transformed into an optimization problem, which minimizes a proposed kernelized objective function with an adaptive spatial constraint term. Differential evolution algorithm is utilized to optimize the kernelized objective function, which uses several differential evolution operators. Experimental results on two remote sensing images show that the proposed algorithm is promising compared with several traditional clustering algorithms.
This work was supported by National Natural Science Foundation of China under Grant No. 41371344, and A Foundation for the Author of National Excellent Doctoral Dissertation of P.R. China (FANEDD) under Grant No. 201052.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhong, Y., Zhang, S., Zhang, L.: Automatic Fuzzy Clustering Based on Adaptive Multi-Objective Differential Evolution for Remote Sensing Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2013), doi:10.1109/JSTARS.2013.2240655
Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging 21(3), 193–199 (2002)
Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 34(4), 1907–1916 (2004)
Filippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognition 41(1), 176–190 (2008)
Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Zhong, Y., Zhang, L.: Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 42(5), 1306–1329 (2012)
Hejazi, H., Mohabati, H., Hosseinian, S., Abedi, M.: Differential evolution algorithm for security-constrained energy and reserve optimization considering credible contingencies. IEEE Transactions on Power Systems 26(3), 1145–1155 (2011)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1999)
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)
Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation 10, 293–298 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, A., Zhong, Y., Zhang, L. (2013). Adaptive Differential Evolution Fuzzy Clustering Algorithm with Spatial Information and Kernel Metric for Remote Sensing Imagery. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-41278-3_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41277-6
Online ISBN: 978-3-642-41278-3
eBook Packages: Computer ScienceComputer Science (R0)