Faculty Publications
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Item 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.Item Hierarchical clustering approaches for flood assessment using multi-sensor satellite images(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2019) Senthilnath, J.; Shreyas, P.B.; Rajendra, R.; Sundaram, S.; Kulkarni, S.; Benediktsson, J.A.In this paper, hierarchical clustering methods are used on synthetic aperture radar (SAR) (during the flood) and LISS-III (before the flood) data to analyse damage caused by floods. The flooded and non-flooded regions are extracted from the SAR image while different land cover regions are extracted from the LISS-III image. Initially, the Bayesian information criterion (BIC) is implemented to obtain the constraints for the number of clusters. The optimal cluster centres are then computed using hierarchical clustering approach (i.e. cluster splitting and merging techniques). The cluster splitting techniques such as Iterative Self-Organising Data Technique (ISODATA), Mean Shift Clustering (MSC), Niche Genetic Algorithm (NGA) and Niche Particle Swarm Optimisation (NPSO) were applied on SAR and LISS-III data. The cluster centres obtained from these algorithms are used to group similar data points by using merging method into their respective classes. Further, the results obtained for each method are overlaid to analyse the individual land cover region that is affected by floods. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.