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首页> 外文期刊>International journal of image and data fusion >Hierarchical clustering approaches for flood assessment using multi-sensor satellite images
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Hierarchical clustering approaches for flood assessment using multi-sensor satellite images

机译：使用多传感器卫星图像进行洪水评估的分层聚类方法

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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.
机译：在本文中，分层聚类方法用于合成孔径雷达（SAR）（洪水期间）和LISS-III（洪水之前）数据，以分析洪水造成的破坏。从SAR图像中提取淹没区域和非淹没区域，而从LISS-III图像中提取不同的土地覆盖区域。最初，实施贝叶斯信息标准（BIC）以获取群集数量的约束。然后使用分层聚类方法（即聚类拆分和合并技术）来计算最佳聚类中心。 SAR和LISS-III数据应用了迭代自组织数据技术（ISODATA），均值漂移聚类（MSC），小生境遗传算法（NGA）和小生境粒子群优化（NPSO）等聚类拆分技术。从这些算法获得的聚类中心通过将合并方法分为各自的类，从而将相似的数据点分组。此外，将每种方法获得的结果叠加起来，以分析受洪水影响的各个土地覆盖区域。

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