Abstract
The ever-growing number of applications for satellites is being compromised by their poor direct positioning precision. Existing orthoimages, such as enhanced thematic mapper (ETM+) orthoimages, can provide georeferences or improve the geo-referencing accuracy of satellite images, such ZY-1-02C images that have unsatisfactory positioning precision, thus enhancing their processing efficiency and application. In this paper, a feasible image matching approach using multi-source satellite images is proposed on the basis of an experiment carried out with ZY-1-02C Level 1 images and ETM+ orthoimages. The proposed approach overcame differences in rotation angle, scale, and translation between images. The rotation and scale variances were evaluated on the basis of rational polynomial coefficients. The translation vectors were generated after blocking the overall phase correlation. Then, normalized cross-correlation and least-squares matching were applied for matching. Finally, the gross errors of the corresponding points were eliminated by local statistic vectors in a TIN structure. Experimental results showed a matching precision of less than two pixels (root-mean-square error), and comparison results indicated that the proposed method outperforms Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Affine-Scale Invariant Feature Transform (A-SIFT) in terms of reliability and efficiency.
Highlights
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. the positioning precision of satellites on a global scale has improved steadily, as seen withZY3, 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
This study focused on the use of multi-source image registration to compensate for the drawbacks of ZY-1-02C in positioning in
Summary
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. ZY3, the overall positioning precision of some satellites is low and unstable owing to different design purposes and hardware configuration deficiencies, restricting their application. 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. 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 in
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