Skeleton Matching with Applications in Severe Weather Detection
Mohammad Mahdi Kamani (1), Farshid Farhat (1),
Stephen Wistar (2),
James Z. Wang (1)
(1) The Pennsylvania State University, USA
(2) Accuweather Inc., USA
Severe weather conditions cause an enormous amount of damages around
the globe. Bow echo patterns in radar images are associated with a
number of these destructive conditions such as damaging winds, hail,
thunderstorms, and tornadoes. They are detected manually by
meteorologists. In this paper, we propose an automatic framework to
detect these patterns with high accuracy by introducing novel
skeletonization and shape matching approaches. In this framework,
first we extract regions with high probability of occurring bow echo
from radar images and apply our skeletonization method to extract the
skeleton of those regions. Next, we prune these skeletons using our
innovative pruning scheme with fuzzy logic. Then, using our proposed
shape descriptor, Skeleton Context, we can extract bow echo features
from these skeletons in order to use them in shape matching algorithm
and classification step. The output of classification indicates
whether these regions are bow echo with over 97% accuracy.
(high-resolution PDF, 14MB)
Mohammad Mahdi Kamani, Farshid Farhat, Stephen Wistar and James
Z. Wang, ``Skeleton Matching with Applications in Severe Weather Detection,''
Applied Soft Computing, vol. 70, pp. 1154-1166, 2018.
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August 31, 2018