Hierarchical clustering approaches for flood assessment using multi-sensor satellite images. (2nd January 2019)

• Record Type: Journal Article
• Authors: Senthilnath, J.; P.B, Shreyas; Rajendra, Ritwik; Suresh, S.; Kulkarni, Sushant; Benediktsson, J.A.
• Is Part Of: International journal of image and data fusion. Volume 10:Number 1(2019)
• Page Start: 28
• Page End: 44
• Publication Date: 2019-01-02
• Subjects: Flood assessment -- Mean Shift Clustering -- Niche Genetic Algorithm -- Niche Particle Swarm Optimisation; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2018.1513956
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: 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.