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Title: Multi-sensor satellite image analysis using niche genetic algorithm for flood assessment
Authors: Senthilnath, J.
Shreyas, P.B.
Rajendra, R.
Omkar, S.N.
Mani, V.
Diwakar, P.G.
Issue Date: 2012
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, Vol.7677 LNCS, , pp.49-56
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. � 2012 Springer-Verlag.
Appears in Collections:2. Conference Papers

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