Multi-sensor satellite image analysis using niche genetic algorithm for flood assessment

Authors:
J. Senthilnath;P. B. Shreyas;Ritwik Rajendra;S. N. Omkar;V. Mani;P. G. Diwakar
Affiliations:
Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India;Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India;Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India;Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India;Earth Observation System, ISRO Headquarters, Bangalore, India
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

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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.