Satellite images from multiple sources with different resolutions are currently able to observe the same region. Reliable image matching between these images is the first step in their integrated use. Image matching of multiple-resolution images is not trivial because of the large geometric differences among the images, which can cause failure of matching and losses of matching accuracy. This paper presents a bound-constrained, multiple-image, least-squares matching (LSM) method that extends the classical LSM in two ways for better performance. First, the a priori metadata of the images, including the geo-referencing and scale information, are used for initial matching and to provide bound constraints in the LSM to improve its stability. Second, multiple images are matched in a single optimization rather than the traditional pairwise matching. This brings additional observations in the least-squares optimization, which makes the matching aware of both larger and local context and improves matching quality even with inaccurate initializations for high resolution images. Experimental analysis using multiple-source satellite images with multiple resolutions collected on Mars and in Hong Kong reveals that the proposed method is capable of obtaining reliable multiple-fold matches effectively, even in challenging cases with resolution differences as much as 20-fold. The method proposed in this paper has significance for the synergistic use of multiple-source satellite images in various applications.