Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Location of tropical cyclone center with intelligent image processing technique
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Information Sciences: an International Journal
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In this paper, an elastic graph dynamic link model (EGDLM) based on elastic contour matching is proposed to automate the Dvorak technique for tropical cyclone (TC) pattern interpretation from satellite images. This method integrates traditional dynamic link architecture (DLA) for neural dynamics and the active contour model (ACM) for contour extraction of TC patterns. Using satellite pictures provided by National Oceanic and Atmospheric Administration (NOAA), 120 tropical cyclone cases that appeared in the period from 1990 to 1998 were extracted for the study. An overall correct rate for TC classification was found to be above 95%. For hurricanes with distinct “eye” formation, the model reported a deviation within 3 km from the “actual eye” location, which was obtained from the aircraft measurement of minimum surface pressure by reconnaissance. Compared with the classical DLA model, the proposed model has simplified the feature representation, the network initialization, and the training process. This leads to a tremendous improvement of recognition performance by more than 1000 times