Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17231
Title: An elastic contour matching model for tropical cyclone pattern recognition
Authors: Lee, RST
Liu, JNK
Keywords: Active contour model (ACM)
Elastic graph dynamic link model
Elastic graph matching
Satellite images
Tropical cyclone pattern recognition
Issue Date: 2001
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2001, v. 31, no. 3, p. 413-417 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: 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.
URI: http://hdl.handle.net/10397/17231
ISSN: 1083-4419
DOI: 10.1109/3477.931532
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