Statistical Clustering for Hierarchical Storm Identification
V. Lakshmanan, NOAA/NSSL and Univ. of Oklahoma, Norman, OK
We describe a recent method of partitioning the pixels of an image such that the partitions at one step are wholly nested inside the partitions of the next step. This is achieved through K-Means clustering followed by region growing and morphological processing. By steadily relaxing the inter-cluster distance between the clusters that is allowed by the morphological processing, a hierarchical tree of clusters is obtained.
We show in this paper that this hierarchical tree of clusters can be used to identify storms at different scales on Weather Service Radar (WSR-88D), Terminal Doppler Weather Radar (TDWR) and on GOES satellite images.
Poster Session 5, Multi-sensor Severe Weather Applications
Tuesday, 13 August 2002, 3:00 PM-4:30 PM
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