# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 5 Discussion
### Image Matching and Error Elimination  
Related studies have presented two representative problems to realize an AMSIM case. One problem is solving the correlation among rotation, scale, and translation in image matching. For multi-source images, the transformation between images is too complex for reliable image matching to be implemented. Numerous approaches, such as SIFT, SURF, and A-SIFT, have been used to perform multi-source image matching. However, they not only consume excessive memory and time but also are unreliable; these are the technological bottlenecks for fully automatic image processing.  
A rough estimation of the transformation between images was achieved via geographic information and OPC-based matching. The most important finding was that the geographic information of images could be used to search for tie points. Tie points were obtained in Datasets 2 and 3 to calculate \(A\) and \(k\) (Table 4). The transformation parameters between the images that were calculated in this way were not very accurate, which we attribute to large topographic variations, image resampling, and processing noise. Given that the viewpoints changed drastically between ZY-1-02C and the DOM of ETM+, the relief parameters were available for the initial matching value prediction. After OPC-based matching, the search region was narrowed. The experimental results proved that the method can solve the problem of transformation between images. Thus, numerous evenly distributed corresponding points could be rapidly obtained and efficiently based on NCC and LSM. The results shown in Figure 5 imply that sufficient conjugate points were obtained. In Section 3.3.4, the matching accuracy was proven to be improved with this image matching strategy. The first experiment also showed that high accuracy matching between ZY-1-02C and ETM+ could only be obtained when the resolutions of the two images were 5 and 15 m.  
The other problem in image matching is error elimination. In most engineering practices, an image matching result contains gross errors. Conventional processing of gross errors is generally categorized into two types: automatic and complex iterative computation in block adjustment [41] and artificial error processing, which are obviously unsuitable for automation in photogrammetry. To solve the problem of automatic elimination of gross errors in image matching, a local statistical estimation, which differs from the approaches reported in most studies, was adopted in this study. Calculating the LSV of the judging points ensured that false matches were detected correctly from the matching results. In the second experiment, the false matches detected in Datasets 2 and 3 were evidently greater in number than the real errors because of the processing noise. Further, the false matches were eliminated in all the results. The high matching precision, which is shown in Figure 10, confirmed the accuracy and practicability of the proposed gross error elimination method.  
Unlike related AMSIM approaches [7; 42], this approach has two different designs for the matching method. Image resampling based on RPCs was first applied to data with uncertain resolution. Second, the matching strategy based on OPC aimed to solve the RPC that contained a positioning error. The main purpose of this study was to obtain a high level of global positioning accuracy for the ZY-1-02C images with the DOM of ETM+ images as georeferences. However, the proposed method also has limitations. Between the input and reference images, the DOM could not handle significant landscape changes or large resolution differences above a ratio of 1:6. Indeed, when satellite images exhibit small differences in positioning accuracy, NCC and LSM can be performed directly, and the corresponding points can be easily searched on the basis of the geographic information of the images.