Tropical hurricanes are among the most devastating hazards on Earth. Knowledge about its intense inner-core structure and dynamics will improve hurricane forecasts and advisories. The precise morphological parameters extracted from high-resolution spaceborne Synthetic Aperture Radar (SAR) images, can play an essential role in further exploring and monitoring hurricane dynamics, especially when hurricanes undergo amplification, shearing, eyewall replacements and so forth. Moreover, these parameters can help to build guidelines for wind calibration of the more abundant, but lower resolution scatterometer wind data, thus better linking scatterometer wind fields to hurricane categories. In this paper, we develop a new method for automatically extracting the hurricane eyes from C-band SAR data by constructing Gray Level-Gradient Co-occurrence Matrices (GLGCMs). The hurricane eyewall is determined with a two-dimensional vector, generated by maximizing the class entropy of the hurricane eye region in GLGCM. The results indicate that when the hurricane is weak, or the eyewall is not closed, the hurricane eye extracted with this automatic method still agrees with what is observed visually, and it preserves the texture characteristics of the original image. As compared to Du’s wavelet analysis method and other morphological analysis methods, the approach developed here has reduced artefacts due to factors like hurricane size and has lower programming complexity. In summary, the proposed method provides a new and elegant choice for hurricane eye morphology extraction.

热带飓风是地球上最具破坏性的灾害之一。了解其强烈的内核结构和动态将改进飓风预报和咨询。从高分辨率星载合成孔径雷达 (SAR) 图像中提取的精确形态参数可以在进一步探索和监测飓风动力学方面发挥重要作用，尤其是在飓风经历放大、剪切、眼壁置换等时。此外，这些参数有助于为更丰富但分辨率较低的散射仪风数据的风校准建立指导方针，从而更好地将散射仪风场与飓风类别联系起来。在本文中，我们开发了一种通过构建灰度级梯度共生矩阵 (GLGCM) 从 C 波段 SAR 数据中自动提取飓风眼的新方法。飓风眼墙由二维向量确定，该向量通过最大化 GLGCM 中飓风眼区域的类熵而生成。结果表明，当飓风较弱或眼壁未闭合时，采用该自动方法提取的飓风眼仍与目视观察一致，保留了原始图像的纹理特征。与杜的小波分析方法和其他形态分析方法相比，这里开发的方法减少了由于飓风大小等因素造成的伪影，并具有较低的编程复杂度。总之，