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.
| Original language | English |
|---|---|
| Pages (from-to) | 28-44 |
| Number of pages | 17 |
| Journal | International Journal of Image and Data Fusion |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2 Jan 2019 |
Bibliographical note
Funding Information: We would like to thank the anonymous reviewers for their comments and suggestions. The authors would like to thank S.N. Omkar and V. Mani from IISc, Bangalore, India, for many useful and stimulating discussions regarding data clustering. Finally, we are indebted to P.G. Diwakar from ISRO, Bangalore, India, for providing the satellite remote sensing data. Publisher Copyright: © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.Other keywords
- Flood assessment
- Mean Shift Clustering
- Niche Genetic Algorithm
- Niche Particle Swarm Optimisation