Browsing by Author "Tejovanth, N."

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Hierarchical clustering algorithm for land cover mapping using satellite images
(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Archana Shenoy, B.
This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust. © 2012 IEEE.
Learning by creating: Interactive programming for Indian high schools
(2012) Gupta, N.; Tejovanth, N.; Murthy, P.
In this paper we discuss results and observations based on empirical studies of introducing programming using Scratch-Arduino to high school students. We analyse the programming experience of students across diverse educational and economic backgrounds, culture and region. Learning of key programming and electronics concepts was measured during the exercise. Results indicate that these fundamentals can be imparted at high schools in the Indian educational context. We find that the introduction of logic programming and computer-hardware interfacing at the high school level is advantageous in terms of creating an interactive environment fostering learning and creativity. � 2012 IEEE.
Learning by creating: Interactive programming for Indian high schools
(2012) Gupta, N.; Tejovanth, N.; Murthy, P.
In this paper we discuss results and observations based on empirical studies of introducing programming using Scratch-Arduino to high school students. We analyse the programming experience of students across diverse educational and economic backgrounds, culture and region. Learning of key programming and electronics concepts was measured during the exercise. Results indicate that these fundamentals can be imparted at high schools in the Indian educational context. We find that the introduction of logic programming and computer-hardware interfacing at the high school level is advantageous in terms of creating an interactive environment fostering learning and creativity. Â© 2012 IEEE.
Multi-spectral satellite image classification using Glowworm Swarm Optimization
(2011) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Shenoy, B, A.
This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall. � 2011 IEEE.
Multi-spectral satellite image classification using Glowworm Swarm Optimization
(2011) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Shenoy B, A.
This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall. Â© 2011 IEEE.