Use of different forms of symmetry and multi-objective optimization for automatic pixel classification in remote-sensing satellite imagery

International Journal of Remote Sensing

Volumn 31, Issue 22, 2010, Pages 5751-5775

  Saha, Sriparna ^a   Bandyopadhyay, Sanghamitra ^a  

^a INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; FUZZY CLUSTERING; IMAGE CLASSIFICATION; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE; PIXELS; SATELLITE IMAGERY; SIMULATED ANNEALING;

AUTOMATIC CLUSTERING TECHNIQUES; FUZZY C MEANS CLUSTERING; FUZZY CLUSTERING TECHNIQUES; LAND COVER CLASSIFICATION; MULTI-OBJECTIVE OPTIMIZATION TECHNIQUES; PARETO OPTIMAL SOLUTIONS; REMOTE SENSING SATELLITES; SEGMENTATION TECHNIQUES;

REMOTE SENSING;

ALGORITHM; CLUSTER ANALYSIS; DATA SET; FUZZY MATHEMATICS; IMAGE CLASSIFICATION; LAND COVER; MULTIOBJECTIVE PROGRAMMING; OPTIMIZATION; PIXEL; REMOTE SENSING; SATELLITE IMAGERY; SEGMENTATION;

EID: 78649841036     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2010.512302     Document Type: Article

References (31)

1. Bandyopadhyay, S. and Saha, S., 2007, GAPS: a clustering method using a new point symmetry based distance measure. Pattern Recognition, 40, pp. 3430-3451.
2. Bandyopadhyay, S. and Saha, S., 2008, A point symmetry based clustering technique for automatic evolution of clusters. IEEE Transactions on Knowledge and Data Engineering, 20, pp. 1-17.
3. Bandyopadhyay, S., Saha, S., Maulik, U. and Deb, K., 2008, A simulated annealing based multi-objective optimization algorithm: AMOSA. IEEE Transactions on Evolutionary Computation, 12, pp. 269-283.
4. Ben-Hur, A. and Guyon, I., 2003, Detecting Stable Clusters Using Principal Component Analysis in Methods in Molecular Biology (New York, NY: Humana Press).
5. Bensaid, A.M., Hall, L.O., Bezdek, J.C., Clarke, L.P., Silbiger, M.L., Arrington, J.A. and Murtagh, R.F., 1996, Validity-guided (re)clustering with applications to image segmentation. IEEE Transactions on Fuzzy Systems, 4, pp. 112-123.
6. Bezdek, J.C., 1973, Fuzzy mathematics in pattern classification. PhD thesis, Cornell University, Ithaca, NY, USA.
7. Bezdek, J.C., 1981, Pattern Recognition with Fuzzy Objective Function Algorithms (New York, NY: Plenum).
8. Bouguila, N. and Ziou, D., 2006, Unsupervised selection of a finite dirichlet mixture model: an MML-based approach. IEEE Transactions on Knowledge and Data Engineering, 18, pp. 993-1009.
9. Comaniciu, D. and Meer, P., 2002, Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, pp. 603-619.
10. Deb, K., 2001, Multi-objective Optimization Using Evolutionary Algorithms (Chichester, UK: John Wiley and Sons, Ltd).
11. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., 2002, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, pp. 182-197.
12. Everitt, B.S., 1993, Cluster Analysis, 3rd edn. (London, UK: Edward Arnold).
13. Geman, S. and Geman, D., 1984, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, pp. 721-741.
14. Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning (New York, NY: Addison-Wesley).
15. Jolliffe, I., 1986, Principal Component Analysis. Springer Series in Statistics (New York, NY: Springer).
16. Kirkpatrik, S., Gelatt, C. and Vecchi, M., 1983, Optimization by simulated annealing. Science, 220, pp. 671-680.
17. Maulik, U. and Bandyopadhyay, S., 2000, Genetic algorithm based clustering technique. Pattern Recognition, 33, pp. 1455-1465.
18. Maulik, U. and Bandyopadhyay, S., 2002, Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, pp. 1650-1654.
19. Maulik, U. and Bandyopadhyay, S., 2003, Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 1075-1081.
20. Maulik, U., Mukhopadhyay, A. and Bandyopadhyay, S., 2009, Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes. BMC Bioinformatics, 10, pp. 1197-1208.
21. Mount, D.M. and Arya, S., 2005, ANN: a library for approximate nearest neighbor searching. Available online at: http://www.cs.umd.edu/,mount/ANN.
22. Pakhira, M.K., Maulik, U. and Bandyopadhyay, S., 2004, Validity index for crisp and fuzzy clusters. Pattern Recognition, 37, pp. 487-501.
23. Pal, S.K., Bandyopadhyay, S. and Murthy, C.A., 2001, Genetic classifiers for remotely sensed images: comparison with standard methods. International Journal of Remote Sensing, 22, pp. 2545-2569.
24. Richards, J.A., 1993, Remote Sensing Digital Image Analysis: an Introduction (New York, NY: Springer-Verlag).
25. Saha, S. and Bandyopadhyay, S., 2008, Fuzzy symmetry based real-coded genetic clustering technique for automatic pixel classification in remote sensing imagery. Fundamenta Informaticae, 84, pp. 471-492.
26. Saha, S. and Bandyopadhyay, S., 2009, A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters. Information Sciences, 179, pp. 3230-3246.
27. Saha, S. and Bandyopadhyay, S., 2010, On principle axis-based line symmetry clustering techniques. Memetic Computing, doi: 10.1007/s12293-010-0049-0.
28. Saha, S. and Maulik, U., 2010, A new line symmetry distance based automatic clustering technique: application to image segmentation. International Journal of Imaging Systems and Technology, in press.
29. Veldhuizen, D.V. and Lamont, G., 2000, Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evolutionary Computation, 2, pp. 125-147.
30. Xie, X.L. and Beni, G., 1991, A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, pp. 841-847.
31. Ziou, D., Bouguila, N., Allili, M.S. and Zaart, A.E., 2009, Finite gamma mixture modeling using minimum message length inference: application to SAR image analysis. International Journal of Remote Sensing, 30, pp. 771-792.