Abstract
High-resolution optical satellites are widely used in environmental monitoring. With the aim to observe the largest possible coverage, the overlapping areas and intersection angles of respective optical satellite images are usually small. However, the conventional bundle adjustment method leads to erroneous results or even failure under conditions of weak geometric convergence. By transforming the traditional stereo adjustment to a planar adjustment and combining it with linear programming (LP) theory, a new method that can solve the bias compensation parameters of all satellite images is proposed in this paper. With the support of freely available open source digital elevation models (DEMs) and sparse ground control points (GCPs), the method can not only ensure the consistent inner precision of all images, but also the absolute geolocation accuracy of the ground points. Tests of the two data sets covering different landscapes validated the effectiveness and feasibility of the method. The results showed that the geo-positioning performance of the method was better in regions of smaller topographic relief or for satellite images with a larger imaging altitude angle. The best accuracy of image geolocation with weak convergence geometry was as high as to 3.693 m in the horizontal direction and 6.510 m in the vertical direction, which is a level of accuracy equal to that of images with good intersection conditions.
Highlights
With advantages of wide coverage, short revisit time, and the appropriate spatial resolution required for large-scale mapping, high-resolution optical satellite imagery is an important means of obtaining global geospatial data
The weak convergence geometry phenomenon is undesirable, as it leads to erroneous results or iteration failures in the classical bundle block adjustment (BBA) method
The situation is ubiquitous in non-mapping optical satellite imagery, which greatly limits the usage of data
Summary
With advantages of wide coverage, short revisit time, and the appropriate spatial resolution required for large-scale mapping, high-resolution optical satellite imagery is an important means of obtaining global geospatial data. Aerial triangulation is based on the principle of the intersection of conjugate rays in overlapped images to undertake bundle block adjustment computation. Its paths and imaging parameters must be designed specially to ensure the ratio of base-to-height and the intersection angle of stereo image pairs meet the requirement for topography mapping. The conjugate rays of image pairs with weak convergence geometry are almost parallel, which leads to erroneous results or iteration failures during the bundle block adjustment (BBA). This undesirable situation greatly limits the use of satellite imagery data. It is necessary to modify the traditional BBA approach to adopt the weakly convergence geometry conditions
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