# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 1 Introduction  
In the application of satellite technology, high-accuracy geolocation and joint observation of multi-source data have emerged as core issues in the fields of photogrammetry and remote sensing. Although the positioning precision of satellites on a global scale has improved steadily, as seen with ZY3, the overall positioning precision of some satellites is low and unstable owing to different design purposes and hardware configuration deficiencies, restricting their application. ZY-1-02C, which is equipped with a multispectral (MS) camera with 10 m resolution, a panchromatic (PAN) camera with 5 m resolution, and a high-resolution PAN camera with 2.36 m resolution, is a satellite that is used for surveying land resources. The overall positioning precision of this satellite imagery, whose processing level is aimed at sensor geometry, is approximately 100 m, and achieves only 1000 m precision under extreme conditions. The conventional approach of manually selecting a large number of control points cannot meet the demands of mass data processing. In contrast, an automatic control point matching method--namely, multi-source satellite image matching--allows the combined processing of different data, providing many possibilities for multi-source applications [1]. This study focused on the use of multi-source image registration to compensate for the drawbacks of ZY-1-02C in positioning inthe sensor geometry processing, resulting in a rich data source and achieving a high-utility earth observation method. Automatic multi-source image matching (AMSIM), which is widely used in photogrammetry and remote sensing, has been studied for decades [2; 3; 4; 5; 6; 7]. Compared to manually measuring control points, image matching can effectively reduce workload and is appropriate for processing mass data. Image matching can be broadly classified into two categories: area-based matching (ABM) and feature-based matching (FBM) [8]. ABM performs a measurement using gray or phase data in a fixed-size window from two images. The most commonly used algorithms are the methods based on mutual information [9; 10], frequency-domain correlation [11], and cross-correlation [12; 13]. However, ABM exhibits low efficiency. In contrast, FBM extracts salient features from images, can achieve sub-pixel accuracy, and facilitates rapid processing. Well-known feature extraction operators [14], SIFT [15], and SURF [16; 17] have been widely used in image matching. Other methods based on features, such as lines [18], edges [8], contours [19], and shapes [20], are also used in numerous applications. Feature points have been shown to exhibit excellent performance and have been successfully applied to various matching cases, including AMSIM [21; 22]. However, some problems remain, such as uneven precision control, mismatch, and high cost. Frequency-domain correlation has also been applied to some AMSIM cases. Phase correlation is known to identify integer pixel displacement. Several Fourier domain methods, and closely-related spatial domain variations, have been proposed for estimating translational shift with sub-pixel accuracy between image pairs [23; 24; 25]. [PERSON] et al. proposed robust phase correlation methods for sub-pixel feature matching, based on the singular value decomposition method [26]. Nevertheless, phase correlation algorithms may have gross errors in matching results due to minor noise and differences between images. Gross errors in image matching--namely, the mismatching of point pairs--need to be addressed. Many experiments [27] have indicated that mismatching is inevitable in areas that contain repeated or insufficient textures, clouds, or shades. Hence, gross error elimination has emerged as another key factor in judging algorithms [28]. Conventional methods for eliminating gross errors are classified into two categories. One method involves complex iterative computation in terms of automatic adjustment [29]. However, it is strongly dependent on the data quality as well as being capital-intensive and unreliable.