Abstract
Bundle adjustment of multi-view satellite images is a powerful tool to align the orientations of all the images in a unified framework. However, the traditional bundle adjustment process faces a problem in detecting mismatches and evaluating low/medium/high-accuracy matches, which limits the final bundle adjustment accuracy, especially when the mismatches are several times more than the correct matches. To achieve more accurate bundle adjustment results, this paper formulates the prior knowledge of matching accuracy as matching confidences and proposes a matching confidence based bundle adjustment method. The core algorithm firstly selects several highest-confidence matches to initially correct orientations of all images, then detects and eliminates the mismatches under the initial orientation guesses and finally formulates both the matching confidences and the forward-backward projection errors as weights in an iterative bundle adjustment process for more accurate orientation results. We compared our proposed method with the famous RANSAC strategy as well as a state-of-the-art bundle adjustment method on the high-resolution multi-view satellite images. The experimental comparisons are evaluated by image checking points and ground control points, which shows that our proposed method is able to obtain more robust and more accurate mismatch detection results than the RANSAC strategy, even though the mismatches are four times more than the correct matches and it can also achieve more accurate orientation results than the state-of-the-art bundle adjustment method.
Highlights
Given multi-view matches, the bundle adjustment (BA) of multi-view satellite images is to align the positions and attitudes of their cameras so that the optical rays from the corresponding pixels intersect at the same ground point in the object space
To achieve accurate BA results, especially in the case of large amounts of mismatches, this paper formulates the prior knowledge of matching accuracy as different matching confidence metrics and proposes a matching confidence based BA method, which is able to remove large amounts of mismatches and adaptively adjust the weights of good matches in BA
Since it may run into the case that a few mismatches have high matching confidences, we adopt inverse proportional model based geometric weights in the initial orientation corrections, which is able to reduce the weights of mismatches
Summary
Given multi-view matches, the bundle adjustment (BA) of multi-view satellite images is to align the positions and attitudes of their cameras so that the optical rays from the corresponding pixels intersect at the same ground point in the object space. Its characteristics of high positioning accuracy have fueled many applications in photogrammetry and remote sensing, for example, digital surface model (DSM) generation [1], image registration [2], survey mapping [3] and so forth. The BA accuracy depend on the matching accuracy. Higher-accuracy matches normally result in higher-accuracy BA results and vice versa. An intuitive idea is to increase the contributions/weights. 2020, 12, 20 of high-accuracy matches and decrease the contributions/weights of low-accuracy matches in the BA process for more accurate BA results. The prior knowledge about the accuracy of each match is essential before BA
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