中文摘要：

高分辨率数据中云高度的差异性突显，特别是边缘处高度在云阴影识别和地表辐射估算等方面成为需要考虑的重要因素。热红外数据获取云高度分辨率较低、缺乏细部差异性特征，为解决这一问题，首先将对应的热红外和可见光数据进行特征点配准，再将基于热红外数据计算的云高度重采样至高分辨率，然后以基于欧式距离变换的围线搜索方法及距离加权将热红外云边缘高度匹配至对应的可见光图像，最后根据云阴影的相似度匹配方法确定真实云高度。结果表明，算法在遵循热红外云高信息分布变化规律的同时，可以得到较准确的高分辨率云边缘高度，一定程度上解决了热红外技术获取云高在分辨率上的局限，扩展了其在云高反演方面的作用。

英文摘要：

Differences in cloud height are obvious in high-resolution data, especially because cloud edge heights have become an important factor in cloud shadow identification and estimation of surface solar radiation. However, the resolution of cloud heights calculated by thermal infrared data is low and lacks detailed characteristics. Cloud edges of visible and thermal infrared bands differ considerably both in shape and geometric features. The edge of high-resolution image has rich characteristics, whereas that of thermal infrared cloud height data is single and fuzzy in geometric characteristics, so they cannot match exactly. Although some feature points of clouds can be obtained by some feature point matching methods such as Scale-Invariant Feature Transform （SIFT） and Harris, the difference between two data on geo- metric features made available by feature points was less. This result cannot satisfy the need to match the information of thermal infrared cloud heights and high-resolution cloud edge data, and obtain a result with abundant diversity. To solve this problem, an algorithm was presented in this study. First, SIFT algorithm was utilized in this method to extract feature points for further image registration and correction. Then, cloud edge heights were calculated by thermal infrared data and re-sampled to a high resolution. Next, Euclidean distance transform was performed for each cloud edge pixel of high-resolution data to all thermal infrared cloud edge pixels, which could obtain spatial relationships between the two types of data. As the two types of data differed considerably in edge characteristics, directly determining the optimal matching point was difficult. Thus, a hierarchical searching method was used here. While the searched objects had different significance to matching points, the weight was given by distance to determine the final matching height. Finally, real cloud heights were determined according to the matching method of cloud shadow similarity. We used five images of HJ-1B CCD and I