Tag Archives: Elsevier

Skeleton Matching with Applications in Severe Weather Detection

Title: Skeleton Matching with Applications in Severe Weather Detection

Authors: Mohammad Mahdi Kamani, Farshid Farhat, Stephen Wistar and James Z. Wang.

Elsevier Journal: Applied Soft Computing, ~27 pages, May 2017.

Abstract:

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.

Full Text: Skeleton_Matching_Severe_Weather_Forecasting

NEWS Highlights:

https://www.sciencedaily.com/releases/2017/06/170621145133.htm

https://www.eurekalert.org/pub_releases/2017-06/ps-nir062117.php

https://phys.org/news/2017-06-leverages-big-severe-weather.html

http://www.sciencenewsline.com/news/2017062217300053.html

Big data provides deadly storm alert