# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 1 Introduction
The other method manually eliminates gross errors and is inapplicable to automatic photogrammetry [30]. [PERSON] et al. proposed an outlier detection method using the Triangulated Irregular Network (TIN) structure, which was successfully applied for automatic registration of terrestrial laser scanning point clouds [31; 32]. This method effectively estimates complex distortions of point cloud data in local areas and eliminates unapparent gross errors in the overall statistics. When conducting AMSIM, existing satellite orthoimages provide a georeference for satellite images that have unsatisfactory positioning precision. Enhanced thematic mapper (ETM+) images, which are acquired from the Landsat 7 platform, have been used for remote sensing [33; 34]. The Landsat 7 satellite, which was launched in 1999, is equipped with an eight-band MS camera with a 15 m resolution. The ETM+ images captured by Landsat 7 have broad coverage and relatively high positioning precision. Thus, Digital Orthophoto Maps (DOMs), which are also called orthorectified imagery, of ETM+ were adopted as georeferenced images in our approach. AMSIM was conducted to achieve high and uniform global positioning accuracy of ZY-1-02C images. Even though the positions of the satellite platforms and sensors are rigorously controlled in relation to satellite images from different sources, errors in rotation, scaling, and translation, relative to the same ground target, still occur in image acquisition, [30; 35]. A strong relationship exists among these errors, which restricts image matching algorithms. The performance of image matching methods depends on the prediction of the initial position of points to be matched and gross error elimination. In other words, predicting initial positions and eliminating gross errors are the two crucial requirements for stable, precise, and effective image matching methods. In this study, AMSIM achieved a co-registration accuracy between ZY-1-02C Level 1 images and the DOMs of ETM+. Corresponding point pairs in image space were first obtained according to the rational polynomial coefficients (RPCs) model. Rotation and scale variances between the images were calculated on the basis of these image point pairs. The ZY-1-02C images were then resampled based on the obtained rotation and scale variances. The resampled images were processed by blocking the overall phase correlation (OPC) with ETM+ orthoimages, to generate the translation parameters of the initial matching. The Harris detector was also applied for extracting feature points from the resampled ZY-1-02C images. These feature points were used in image matching. Normalized cross-correlation (NCC) and least-squares matching (LSM) were used for image matching between the resampled ZY-1-02C image and ETM+'s DOM. Finally, the matched points were assembled to construct a triangle network structure. According to the matched points, the local statistic vectors (LSV) of the point pairs were obtained statistically to detect and eliminate the gross errors. The experimental results indicate that the proposed approach can achieve stable and correct matching when the resolution difference is not large (1:3). The matching results of the ZY-1-02C image showed a positioning precision of <10 m.