CHANGE DETECTION OF REMOTE SENSING IMAGES USING LEVY FLIGHT PSO

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Josephina Paul

Abstract

In this paper, we propose an improved fusion technique for the change detection of remote sensing images. Two difference images, one log ratio image and other a difference image generated by subtraction were fused in the undecimated wavelet domain, as UWT is good in representing images into multiscale, pyramidal form. The resultant sub band images were segmented with a Particle Swarm Optimization algorithm with Levy flights due to its robustness against local optima, unlike the standard PSO. The accuracy metrics - Percent Correct Clustering (PCC) and Kappa statistic - were used to compare the performance of the proposed method with a few other algorithms and found to be outperforming.

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References

Macleod, R. D., And Congalton, R. G., 1998, A quantitative comparison of change detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 64, 207–216.

D. Lu, P. Mausel, E. Brondi´Zio And E. Moran, Change detection Techniques, Int. J. Remote Sensing, 2003, Vol. 25, No. 12, 2365–2407.

Maoguo Gong, Member, Zhiqiang Zhou, and Jingjing Ma, Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering, IEEE Transactions on Image Processing, Vol. 21, No. 4, April 2012

Turgay Celik and Kai-Kuang Ma, Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform, IEEE Transactions On Geoscience And Remote Sensing, Vol. 48, No. 3, 2010

R. S. Blum, “Robust image fusion using a statistical signal processing approach,†Information Fusion, vol. 6, no. 2, pp. 119–128, 2005.

S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed., Academic Press, 1999.

J Kennedy, RC Eberhart, “Particle Swarm Optimizationâ€, Proceedings of the IEEE International Joint Conference on Neural Networks, Vol. 4, pp 1942–1948, 1995.

Kennedy J and Eberhart R (2001) Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA.

X.H. Shi, Y.C. Liang, H.P. Lee, C. Lu, L.M. Wang, An improved GA and a novel PSO-GA-based hybrid algorithm, Inf. Process. Lett. 93 (2005) 255–261.

H. Haklı, H. Uguz, A novel particle swarm optimization algorithm with Levyflight, Appl. Soft Comput. (23) (2014) 333–345.

R. Jensi, G. Wiselin Jiji, An enhanced particle swarm optimization with levy flight for global Optimization, Applied Soft Computing 43 (2016) 248–261

G.M. Viswanathan, V. Afanasyev, S.V. Buldyrev, E.J. Murphy, P.A. Prince, H.E.Stanley, Lévy flight search patterns of wandering albatrosses, Nature 381 (1996)413–415.