环境与灾害监测预报小卫星星座（环境一号卫星，HJ-1A/B）自发射以来，在环境监测、灾害评估、土地资源调查等领域发挥了重要的作用。但是HJ-1A/B 卫星CCD图像的2级产品（HJ-1 CCD图像）几何精度低，实际应用中需要进行几何精校正。HJ-1 CCD图像具有宽覆盖、大视场角、几何变形复杂的特点，几何精校正难度大。针对该问题，本文提出了一个以Landsat TM全球拼接图像为基准，基于Forstner算子和模板匹配的分层配准方法。该方法使用分层匹配获得的大量高精度且分布均匀的控制点构建Delaunay三角网，有效地解决了HJ-1 CCD图像的几何精校正问题。在配准技术研究的基础上，研发了HJ-1 CCD图像几何精校正系统，系统具有全球HJ-1 CCD图像的自动批量处理能力。实验结果表明，本文提出的几何精校正方法精度高，实现了环境星图像的自动批量处理。

Two optical satellites HJ-1A and HJ-1B play an important role in natural disaster monitoring and risk assessment, environment monitoring, and other fields. However, the system geometric correction products of HJ-1 CCD have low geometric precision and need to be corrected. HJ-1 CCD images have a larger aspect angle, a wider swath width and a larger image size. Furthermore, local geometric distortions are too complex in one scene. Thus, the geometric correction is difficult. In this paper, a hierarchical approach to automatic registration between HJ-1 CCD images and Landsat TM images based on the combination of Forstner operator and template matching is proposed firstly. The approach can obtain a large number of Control Points (CPs) with high precision and even distribution. These CPs are used to generate Delaunay TIN. Then, local distortions of HJ-1 images can be rectified with the Delaunay triangulated irregular network (Delaunay TIN). A geometric precise correction system is then implemented, thereby completing the automatic processing for global HJ-1 images. Experiments with six HJ-1 images demonstrate that the approach is efficient, accurate, and fully automatic.