Abstract

Image segmentation is defined as the process of dividing an image into disjoint homogenous regions and it could be regarded as the fundamental step in various image processing applications. In this paper, a novel multilevel thresholding segmentation method is proposed for grouping the pixels of remote sensing (RS) images into different homogenous regions. In this way, Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO) is used for finding the optimal set of threshold values. The new method is tested on two different study areas and results are compared with PSO-based image segmentation comprehensively. Results show HGAPSO based image segmentation performs better than PSO-based method in different points of view.

References

• D. B. Fogel, “Evolutionary computation: Toward a new philosophy of machine intelligence, Second edition, Piscataway, NJ: IEEE Press, 2000.
• A.D. Brink, “Minimum spatial entropy threshold selection”, IEE Proceedings on Vision Image and Signal Processing 142 (1995), 128-132.
• R. V. Kulkarni and G. K. Venayagamoorthy Bio-Inspired Algorithms for Autonomous Deployment and Localization of Sensor, IEEE trans. On systems, vol. 40, no. 6, 2010. 663-675.
• N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst., Man Cybern., vol. SMC-9, no. 1, pp. 62–66, Jan. 1979.
• C. C. Lai, D. C. Tseng, “A hybrid approach using Gaussian smoothing and genetic algorithm for multilevel thresholding”, International Journal of Hybrid Intelligent Systems 1(3) (2004),143-152.
• P. Y. Yin, “A fast scheme for optimal thresholding using genetic algorithms”, Signal processing 72 (1999), 85−95.
• Yi-Tung Kao, Erwie Zahara and I-Wei Kao, “ A hybridized approach to data clustering,” Expert Systems with Applications 34 (2008), 1754-1762.
• D. Beasley, D. Bull, and R. Martin, An overview of genetic algorithms. Univ. Camping 15 (2), 1993, no. 58-69, part 1.
• M.Frieke, B. V. Coillie, P.Lieven, C. Verbeke, and R.Robert, “selection by genetic algorithms in object-based classification of ikonos imagery for forest mapping in flanders,” Remote Sensing of Environment, vol. 110, no. 7, pp. 476–487, Jul 2007.
• D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning. Massachusetts, USA: Addison-Wesley Longman., 1989
• J. Kennedy, and R. Eberhart, “Particle Swarm Optimization”, Proceedings of the IEEE conference on neural networks ICNN‟95, Perth, Australia 4 (1995), 1942-1948.
• P. Y. Yin, “A fast scheme for optimal thresholding usinggenetic algorithms”, Signal processing 72 (1999), 85−95.
• C. F. Luand C. F. Juang., “Evolutionary fuzzy control of flexible ac transmission system,” IEE Proc.-Gener. Transm. Distrib., vol. 152, no. 4, pp. 441–448, 2005.
• P.D. Sathya and R. Kayalvizhi, PSO based tsallis tresholding selection procedure for image segmentation, International Journal of Computer Applications, Vol.5, No.4, pp. 39-46, 2010.