Abstract
In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient.
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References
Duc, V.B.: Advantage of the remote sensing data utilization in studying inundation risks in terms of Land-use. In: IEEE Int. Conf. on Geoscience and Remote Sens. Symp., pp. 279–282 (2006)
Zhou, C.H., Luo, J.C., Yang, C.J., Li, B.L., Wang, S.L.: Flood monitoring using multi-temporal AVHRR and RADARSAT imagery. Photogrammetric Engineering and Remote Sensing 66(5), 633–638 (2000)
Freeman, A., Durden, S.: A three component scattering model for polarimetric SAR data. IEEE Tran. of Geoscience and Remote Sens., 36963–36973 (1998)
Shamaoma, H., Kerle, N., Alkema, D.: Extraction of flood-modelling related base-data from multi-source remote sensing imagery. In: ISPRS Mid-Term Symp. Remote Sens.: From Pixels to Processes, May 8-11. ITC, Enschede (2006)
Moungjin, L., Seongwoo, J., Soojyoung, M., Juongsun, W.: Detecting Flooded Location Using SAR Data and Assessment of Post-Flooded Condition. In: ACRS Proceedings (2008)
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. J. Royal Statistical Society. Series C (Applied Statistics) 28(1), 100–108 (1979)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)
Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., Shenoy, B.A.: Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images. IEEE Journal of Selected Topics in Appl. Earth Observations and Remote Sens. 5(3), 762–768 (2012)
Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Grefenstette, J.J. (ed.) Genetic Algorithms and their Application, pp. 41–49. Lawrence Erlbaum, Hillsdale (1987)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Deb, K., Goldberg, D.E.: An investigation of niche and species-formationin genetic function optimization. In: Schaffer, J.D. (ed.) Proc. 3rd Int. Conf. Genetic Algorithms, San Mateo, CA, pp. 42–50 (1989)
Brits, R., Engelbrecht, A.P., Van den Bergh, F.: A niching particle swarm optimizer. In: Proc. 4th Asia-Pacific Conf. Simulated Evolutionand Learning, pp. 692–696 (2002)
Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evolutionary Computation 8(3), 256–279 (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, pp. 1942–1948 (1995)
Das, S., Maity, S., Qu, B.-Y., Suganthan, P.N.: Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art 1(2), 71–88 (2011)
Qu, B.-Y., Suganthan, P.N., Liang, J.J.: Differential Evolution with Neighborhood Mutation for Multimodal Optimization. Accepted by IEEE Trans. on Evolutionary Computation, doi:10.1109/TEVC.2011.2161873
Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)
Rosenfield, G.H., Fitzpatrick-Lins, K.: A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing 52(2), 223–227 (1986)
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Senthilnath, J., Shreyas, P.B., Rajendra, R., Omkar, S.N., Mani, V., Diwakar, P.G. (2012). Multi-sensor Satellite Image Analysis Using Niche Genetic Algorithm for Flood Assessment. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_7
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DOI: https://doi.org/10.1007/978-3-642-35380-2_7
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