Efficient Land-cover Segmentation Using Meta Fusion
الموضوعات : Image ProcessingMorteza Khademi 1 , Hadi Sadoghi yazdi 2
1 - Ferdowsi University of Mashhad
2 - Ferdowsi University of Mashhad
الکلمات المفتاحية: Fusion , Land-cover Segmentation , Multiple High-spatial Resolution Panchromatic Remotely Sensed (HR-PRS) Images , Fuzzy C-means (FCM),
ملخص المقالة :
Most popular fusion methods have their own limitations; e.g. OWA (order weighted averaging) has “linear model” and “summation of inputs proportions in fusion equal to 1” limitations. Considering all possible models for fusion, proposed fusion method involve input data confusion in fusion process to segmentation. Indeed, limitations in proposed method are determined adaptively for each input data, separately. On the other hand, land-cover segmentation using remotely sensed (RS) images is a challenging research subject; due to the fact that objects in unique land-cover often appear dissimilar in different RS images. In this paper multiple co-registered RS images are utilized to segment land-cover using FCM (fuzzy c-means). As an appropriate tool to model changes, fuzzy concept is utilized to fuse and integrate information of input images. By categorizing the ground points, it is shown in this paper for the first time, fuzzy numbers are need and more suitable than crisp ones to merge multi-images information and segmentation. Finally, FCM is applied on the fused image pixels (with fuzzy values) to obtain a single segmented image. Furthermore mathematical analysis and used proposed cost function, simulation results also show significant performance of the proposed method in terms of noise-free and fast segmentation.
[1] S. Saha and S. Bandyopadhyay, "Application of a New Symmetry-Based Cluster Validity Index for Satellite Image Segmentation," IEEE Geoscience and Remote Sensing Letters, vol. 5, pp. 166-170, 2008.#
[2] S. Mitra, M. Dickens, and S. Pemmaraju, "Adaptive Clustering for Segmentation of Multi-sensor Images," 1998.#
[3] M. Z. Ahmed REKIK, Ahmed Ben Hamida, Mohammed Benjelloun, "Review of satellite image segmentation for an optimal fusion system based on the edge and region approaches," International Journal of Computer Science and Network Security, vol. 7, pp. 242-250, 2007.#
[4] I. Saha, U. Maulik, S. Bandyopadhyay, and D. Plewczynski, "SVMeFC: SVM Ensemble Fuzzy Clustering for Satellite Image Segmentation," Geoscience and Remote Sensing Letters, IEEE, vol. 9, pp. 52-55, 2012.#
[5] G. Wang, G. Z. Gertner, S. Fang, and A. B. Anderson, "A methodology for spatial uncertainty analysis of remote sensing and GIS products," Photogrammetric Engineering & Remote Sensing, vol. 71, pp. 1423-1432, 2005.#
[6] C. T. Hunsaker, M. F. Goodchild, M. A. Friedl, and T. J. Case, Spatial uncertainty in ecology: implications for remote sensing and GIS applications: Springer Science & Business Media, 2013.#
[7] H. T. Mowrer and R. G. Congalton, Quantifying spatial uncertainty in natural resources: theory and applications for GIS and Remote Sensing: CRC Press, 2003.#
[8] T. Cheng, "Fuzzy objects: their changes and uncertainties," Photogrammetric Engineering and Remote Sensing, vol. 68, pp. 41-50, 2002.#
[9] F. Wang and G. B. Hall, "Fuzzy representation of geographical boundaries in GIS," International Journal of Geographical Information Systems, vol. 10, pp. 573-590, 1996.#
[10] G. Foody, "Fuzzy modelling of vegetation from remotely sensed imagery," Ecological modelling, vol. 85, pp. 3-12, 1996.#
[11] X. Zhao, A. Stein, and X. Chen, "Application of random sets to model uncertainties of natural entities extracted from remote sensing images," Stochastic Environmental Research and Risk Assessment, vol. 24, pp. 713-723, 2010.#
[12] P. Fisher, T. Cheng, and J. Wood, "Higher order vagueness in geographical information: Empirical geographical population of type n fuzzy sets," Geoinformatica, vol. 11, pp. 311-330, 2007.#
[13] T. Cheng and M. Molenaar, "Objects with fuzzy spatial extent," Photogrammetric Engineering and Remote Sensing, vol. 65, pp. 797-802, 1999.#
[14] G. Rees, Physical principles of remote sensing vol. 1: Cambridge Univ Pr, 2001.#
[15] E. Yasunori, T. Isao, H. Yukihiro, and M. Sadaaki, "Kernelized fuzzy c-means clustering for uncertain data using quadratic penalty-vector regularization with explicit mappings," in Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, 2011, pp. 804-809.#
[16] B. Kao, S. D. Lee, D. W. Cheung, W.-S. Ho, and K. Chan, "Clustering uncertain data using voronoi diagrams," in Data Mining, 2008. ICDM&39;08. Eighth IEEE International Conference on, 2008, pp. 333-342.#
[17] G. B. Heuvelink, J. D. Brown, and E. Van Loon, "A probabilistic framework for representing and simulating uncertain environmental variables," International Journal of Geographical Information Science, vol. 21, pp. 497-513, 2007.#
[18] X. Yu, H. He, D. Hu, and W. Zhou, "Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm," Science China Earth Sciences, vol. 57, pp. 1306-1313, 2014/06/01 2014.#
[19] A. Stein, N. Hamm, and Q. Ye, "Handling uncertainties in image mining for remote sensing studies," International journal of remote sensing, vol. 30, pp. 5365-5382, 2009.#
[20] Y. El-Sonbaty and M. A. Ismail, "Fuzzy clustering for symbolic data," IEEE Transaction on Fuzzy Systems, vol. 6, pp. 195–204, 1998.#
[21] S. Lee and M. Crawford, "Unsupervised classification for multi-sensor data in remote sensing using Markov random field and maximum entropy method," in IEEE 1999 International Geoscience and Remote Sensing Symposium, 1999. IGARSS&39;99 Proceedings., 1999, pp. 1200-1202.#
[22] S. Lee, A. Suh, and M. Jung, "Multi-sensor data classification in remote sensing using MRF regional growing algorithm," in IEEE 2001 International Geoscience and Remote Sensing Symposium, 2001. IGARSS&39;01., 2001, pp. 2884-2886.#
[23] S. Lee and M. Crawford, "Multi-channel/multi-sensor image classification using hierarchical clustering and fuzzy classification," in IEEE 2000 International Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000., 2000, pp. 957-959.#
[24] S. Nazarko, "Evaluation of data fusion methods using Kalman filtering and transferable belief model," Master’&39;s thesis, University of Jyv&228;skyl&228;, 2002.#
[25] X. Dai and S. Khorram, "Data fusion using artificial neural networks: a case study on multitemporal change analysis," Computers, Environment and Urban Systems, vol. 23, pp. 19-31, 1999.#
[26] H. Ghassemian, "Multisensor Image Fusion by Inverse Subband Coding," Proceeding of ISPRS-2000, CD, vol. 3.#
[27] M. Hasanzadeh and S. Kasaei, "A Multispectral Image Segmentation Method Using Size-Weighted Fuzzy Clustering and Membership Connectedness," IEEE Geoscience and Remote Sensing Letters, vol. 7, 2010.#
[28] A. A. Naeini, S. Niazmardi, S. R. Namin, F. Samadzadegan, and S. Homayouni, "A Comparison Study Between Two Hyperspectral Clustering Methods: KFCM and PSO-FCM," in Computational Intelligence and Decision Making, ed: Springer, 2013, pp. 23-33.#
[29] A. Bendjebbour, L. Fouque, V. Samson, and W. Pieczynski, "Multisensor Image Segmentation Using Dempster–Shafer Fusion in Markov Fields Context," IEEE Transaction on Geoscience and Remote Sensing, vol. 38, pp. 1789-1798, 2001.#
[30] B. Benmiloud and W. Pieczynski, "Estimation des parameters dans les chaines de Markov cachees et segmentation d&39;images," Traitement du signal, vol. 12, pp. 433-454, 1995.#
[31] N. Giordana and W. Pieczynski, "Estimation of generalized multisensor hidden Markov chains and unsupervised image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 465-475, 1997.#
[32] A. Bendjebbour and W. Pieczynski, "Multisensor Evidential Hidden Markov Fields and Image Segmentation," presented at the Second IEEE Interantional Conference on Intelligent Processing Systems (ICIPC&39;98), Australia, 1998.#
[33] N. Giordana and W. Pieczynski, "Unsupervised segmentation of multisensor images using generalized hidden Markov chains," in International Conference on Image Processing, 1996. Proceedings., 1996, pp. 987-990 vol.3.#
[34] M. S. Balch, "Methods for Rigorous Uncertainty Quantification with Application to a Mars Atmosphere Model," Virginia Polytechnic Institute and State University, 2010.#
[35] M. Hadi, K. Morteza, and S. Y. Hadi, "Vector fuzzy C-means," Journal of Intelligent and Fuzzy Systems, vol. 24, pp. 363-381, 2013.#
[36] K. L. Wu and M. S. Yang, "Alternative c-means clustering algorithms," Pattern Recognition, vol. 35, pp. 2267-2278, 2002.#
[37] C. C. Chuang, J. T. Jeng, and C. W. Li, " Fuzzy C-Means Clustering Algorithm with Unknown Number of Clusters for Symbolic Interval Data," presented at the SICE Annual Conference, 2008.#
[38] S. Saha and S. Bandyopadhyay, "Application of a Multiseed-Based Clustering Technique for Automatic Satellite Image Segmentation," IEEE Geoscience and Remote Sensing Letters, vol. 7, pp. 306-308, 2010.#
[39] S. Das and S. Sil, "Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm," Information Sciences, vol. 180, pp. 1237–1256, 2010.#
[40] X. L. Xie and G. Beni, "A validity measure for fuzzy clustering," IEEE Transactions on pattern analysis and machine intelligence, vol. 13, pp. 841-847, 1991.#
[41] S.-B. Cho and S.-H. Yoo, "Fuzzy Bayesian validation for cluster analysis of yeast cell-cycle data," Pattern recognition, vol. 39, pp. 2405-2414, 2006.#
[42] P. D&39;Urso and P. Giordani, "A weighted fuzzy c-means clustering model for fuzzy data," Computational Statistics & Data Analysisvol, vol. 50, pp. 1496-1523, 2006.#
[43] F. A. T. D. Carvalho, "Fuzzy clustering algorithms for symbolic interval data based on adaptive and non-adaptive Euclidean distances," in Proceedings of the Ninth Brazilian Symposium on Neural Networks (SBRN&39;06), 2006, pp. 60-65.#
[44] F. A. T. D. Carvalho, "Fuzzy c-means clustering methods for symbolic interval data," Pattern Recognition Letters, vol. 28, pp. 423–437, 2007.#
[45] M. Ben Salah, A. Mitiche, and I. Ben Ayed, "Effective level set image segmentation with a kernel induced data term," Image Processing, IEEE Transactions on, vol. 19, pp. 220-232, 2010.#
[46] D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, and V. Osuna, "A Multilevel Thresholding algorithm using electromagnetism optimization," Neurocomputing, vol. 139, pp. 357-381, 2014.#
[47] D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, and M. Perez-Cisneros, "Multilevel thresholding segmentation based on harmony search optimization," Journal of Applied Mathematics, vol. 2013, 2013.#
[48] H. Greenspan, A. Ruf, and J. Goldberger, "Constrained Gaussian mixture model framework for automatic segmentation of MR brain images," Medical Imaging, IEEE Transactions on, vol. 25, pp. 1233-1245, 2006.#
[49] M. L. Comer and E. J. Delp, "The EM/MPM algorithm for segmentation of textured images: Analysis and further experimental results," Image Processing, IEEE Transactions on, vol. 9, pp. 1731-1744, 2000.#
