Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Senthilnath, J.Subject: search.filters.subject.Niche particle swarm optimization

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    Quasi-based hierarchical clustering for land cover mapping using satellite images
    (Springer Verlag service@springer.de, 2013) Senthilnath, J.; Raj, A.; Omkar, S.N.; Mani, V.; Kumar, D.
    This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC).The main objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach-NPSO with quasi distribution(Faure).These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification. © 2013 Springer.
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    Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2015) Senthilnath, J.; Omkar, S.N.; Mani, V.; Prasad, R.; Rajendra, R.; Shreyas, P.B.
    This paper investigates a new approach for flood evaluation based on multi-sensor satellite images utilizing swarm intelligence techniques. The swarm intelligence techniques used are Genetic Algorithm (GA) for image registration and Niche Particle Swarm Optimization (NPSO) for image clustering. Analysis of satellite images are applied in two stages: Linear Imaging Self Scanning Sensor (LISS-III) image acquired before-flood and Synthetic Aperture Radar (SAR) image acquired during-flood. In the first step, SAR image is aligned with LISS-III image using GA. The aligned SAR image (during-flood) is used to extract flooded and non-flooded regions where as LISS-III image (before-flood) is used to classify various land cover regions. For this image clustering is carried out where cluster centers are generated using the cluster splitting technique such as NPSO. The data points are grouped into their respective classes using the merging method. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. The performance comparisons of these swarm intelligence techniques with conventional methods are presented. © 2015 IEEE.