Abstract

Nadir viewing satellite image is an effective data source to generate orthomosaics. Because of the georeferencing error of satellite images, block adjustment is the first step of orthomosaic generation over a large area. However, the geometric relationship of the neighboring orbits of the nadir viewing images is not rigid enough. This paper proposes a new rational function model (RFM) block adjustment approach that constrains the tie point elevation to enhance the relative geometric rigidity. By interpolating the elevations of tie points in a digital elevation model (DEM) and estimating the a priori errors of the interpolated elevations, better overall relative accuracy is obtained, and the local optimal solution problem is avoided. By constraining the adjusted model parameters according to the a priori error of RFMs, block adjustment without ground control point (GCP) is performed. By optimal initializing the object–space positions of tie points with multi-backprojection method, the needed iteration times of block adjustment are reduced. The proposed approach is investigated with 46 Ziyuan-3 sensor-corrected images, a 1:50 000 scale DEM, and 586 GCPs. Compared with Teo's approach that constrains the horizontal coordinates and elevations of tie points, the approach in this paper converges much faster when the GCPs are sparse, and meanwhile, the absolute and relative accuracy of the two approaches are almost the same. The result of block adjustment with only four GCPs shows that no accuracy degeneration occurred in the test area and the root-mean-square error of independent check point reaches about 1.5 ground resolutions. Different DEMs and number of tie points are used to investigate whether the block adjustment result is influenced by these factors. The results show that better DEM accuracy and denser tie points do improve the accuracy when the images have large side-sway angles. The proposed approach is also tested with 5118 IKONOS-2 images that cover the southern Europe without GCP. The result shows that the relative mosaicking accuracy is much better than that of Grodecki's approach.

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