Abstract
This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Islam, A.K., Bala, S.K. and Haque, A.: Flood inundation map of Bangladesh using MODIS surface reflectance data. 2nd Intl. Conf. on Water & Flood Management. (2009).
Zhan, X., Sohlberg, R.A., Townshend, J.R.G., DiMiceli, C., Carroll, M.L., Eastman, J.C.: Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment. 83, 336−350 (2002).
Khan, S.I., Hong, Y., Wang, J., Yilmaz, K.K., Gourley, J.J.,Adler, R.F., Brakenridge, G.R., Policelli, F., Habib, S., and Irwin, D.: Satellite remote sensing and hydrologic modelling for flood inundation mapping in lake Victoria Basin: Implications for hydrologic prediction in ungauged basins, IEEE Tran.on Geoscience and Remote Sensing. 49, 85–95 (2011).
Bosco, G.L.: A genetic algorithm for image segmentation. Proc. IEEE 11thIntl Conf. on Image Analysis and Processing. 262-266 (2001).
Nagesh, K.D., Janga R.M.: Multipurpose reservoir operation using particle swarm optimization. J Water Resource Plan Manage ASCE. 133,192–201 (2007).
Huang, W., Zhang, X.: Projection Pursuit Flood Disaster Classification Assessment Method Based on Multi-Swarm Cooperative Particle Swarm Optimization, Journal of Water Resource and Protection. 3, 415-420 (2011).
Omran, M.G., EngelBrecht, A.P., Salman, A.A.: Particle swarm optimization for pattern recognition and image processing. Swarm Intelligence in Data Mining, 34, 125-151(2006).
Mingjun, S., Daniel, C.: Road extraction using SVM and image segmentation. Photogrammetric Engineering & Remote Sensing. 70 (12), 1365–1371 (2004).
Hamerly, G., Elkan, C.: Alternatives to the K-means algorithm that find better Clusterings. Proc. of the ACM Conf. on Inform and Knowledge Mgmt. 600–607 (2002).
Senthilnath, J., Shivesh, B., Omkar, S.N., Diwakar, P.G., Mani, V.: An approach to Multi-temporal MODIS Image analysis using Image classification and segmentation. Advances in Space Research. 50(9), 1274 – 1287 (2012).
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In Proc. of IEEE Intl. Conf.on Neural Networks. 4, 1942–1948 (1995).
Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. Technical Report HPL-2003-4, HP Labs. (2006).
Sanyal, J., Lu, X. X.: Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India. Hydrological Processes 19, 3699–3716 (2005).
Macqueen. J.: Some methods for classification and analysis of multi-variate observations. In Proc. 5th Berkeley Symp. 281-297 (1967).
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEETrans. Pattern Anal.Mach. Intell., 24 (5), 603–619 (2002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Senthilnath, J., Vikram Shenoy, H., Omkar, S.N., Mani, V. (2013). Spectral-Spatial MODIS Image Analysis Using Swarm Intelligence Algorithms and Region Based Segmentation for Flood Assessment. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 202. Springer, India. https://doi.org/10.1007/978-81-322-1041-2_14
Download citation
DOI: https://doi.org/10.1007/978-81-322-1041-2_14
Published:
Publisher Name: Springer, India
Print ISBN: 978-81-322-1040-5
Online ISBN: 978-81-322-1041-2
eBook Packages: EngineeringEngineering (R0)