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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Identification of Tropical Cyclone Centers in SAR Imagery Based on Template Matching and Particle Swarm Optimization Algorithms
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Identification of Tropical Cyclone Centers in SAR Imagery Based on Template Matching and Particle Swarm Optimization Algorithms

机译：基于模板匹配和粒子群算法的SAR图像热带气旋中心识别

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摘要

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.
机译：合成孔径雷达（SAR）通过提供有关TC中心位置的信息，已成为监视热带气旋（TC）的新工具。但是，由于SAR的条带宽度有限，它通常不能覆盖整个TC域。在本文中，当SAR图像仅覆盖TC的雨带部分而不覆盖眼睛时，我们将开发一种程序来确定TC中心的位置。该算法基于图像处理过程和TC固有雨带结构的可用知识。该三步算法包括：1）应用Canny边缘检测器找到与雨带相关的曲线； 2）定义两个滤波器标准，以选择类似于基于TC雨带模型的估计的螺旋曲线； 3）使用粒子群优化算法寻找最优匹配解。在没有TC眼睛信息的情况下进行图像的数值实验表明，该方法可以有效地定位TC的中心。我们将实验结果与最佳跟踪数据进行比较，以表明准确性。然后，我们比较了流入角模型和对数螺旋模型，发现流入角模型对于TC中心识别更为准确。

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