Identification of Tropical Cyclone Centers in SAR Imagery Based on Template Matching and Particle Swarm Optimization Algorithms

• Published in: IEEE transactions on geoscience and remote sensing Vol. 57; no. 1; pp. 598 - 608
• Main Authors: Jin, Shaohui, Li, Xiaofeng, Yang, Xiaofeng, Zhang, Jun A., Shen, Dongliang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Atmospheric precipitations; Cyclones; Filtering; Hurricanes; Identification; Image processing; Imagery; Inflow; Mathematical models; Monitoring; Particle swarm optimization; Pattern matching; Rain; SAR (radar); Satellites; Spirals; storms; Swath width; Synthetic aperture radar; synthetic aperture radar (SAR); Template matching; Tropical climate; Tropical cyclones; Wind speed
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2018.2863259

Synthetic aperture radar (SAR) has emerged as a new tool for tropical cyclone (TC) monitoring by providing information on the location of TC centers. However, SAR does not usually cover the entire TC domain due to its limited swath width. In this paper, we develop a procedure to identify the location of the center of a TC when an SAR image only covers the rain band portion of the TC but not the eye. The algorithm is based on both an image processing procedure and the available knowledge of the inherent rain-band structure of a TC. The three-step algorithm includes: 1) applying a Canny edge detector to find the curves associated with rain bands; 2) defining two filter criteria to select the spiral curves that resemble the estimation based on a TC rain-band model; 3) searching for the optimal matching solution using the particle swarm optimization algorithm. Numerical experiments with images without TC eye information show that the proposed method can effectively locate the centers of TCs. We compare the experimental results with the best track data to indicate the accuracy. Then, we compare the inflow angle model and the logarithmic spiral model and find that the inflow angle model is more accurate for TC center identification.