A New Matching Approach For Three-Line Scanner Imagery (original) (raw)
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Automatic image orientation of close-range image blocks is becoming a task of increasing importance in the practice of photogrammetry. Although image orientation procedures based on interactive tie point measurements do not require any preferential block structure, the use of structured sequences can help to accomplish this task in an automated way. Automatic orientation of image sequences has been widely investigated in the Computer Vision community. Here the method is generally named "Structure from Motion" (SfM), or "Structure and Motion". These refer to the simultaneous estimation of the image orientation parameters and 3D object points of a scene from a set of image correspondences. Such approaches, that generally disregard camera calibration data, do not ensure an accurate 3D reconstruction, which is a requirement for photogrammetric projects. The major contribution of SfM is therefore viewed in the photogrammetric community as a powerful tool to automatically provide a dense set of tie points as well as initial parameters for a final rigorous bundle adjustment. The paper, after a brief overview of automatic procedures for close-range image sequence orientation, will show some characteristic examples. Although powerful and reliable image orientation solutions are nowadays available at research level, there are certain questions that are still open. Thus the paper will also report some open issues, like the geometric characteristics of the sequences, scene's texture and shape, ground constraints (control points and/or free-network adjustment), feature matching techniques, outlier rejection and bundle adjustment models.
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Automatic Orientation of Large Blocks of Oblique Images
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013
Nowadays, multi-camera platforms combining nadir and oblique cameras are experiencing a revival. Due to their advantages such as ease of interpretation, completeness through mitigation of occluding areas, as well as system accessibility, they have found their place in numerous civil applications. However, automatic post-processing of such imagery still remains a topic of research. Configuration of cameras poses a challenge on the traditional photogrammetric pipeline used in commercial software and manual measurements are inevitable. For large image blocks it is certainly an impediment. Within theoretical part of the work we review three common least square adjustment methods and recap on possible ways for a multi-camera system orientation. In the practical part we present an approach that successfully oriented a block of 550 images acquired with an imaging system composed of 5 cameras (Canon Eos 1D Mark III) with different focal lengths. Oblique cameras are rotated in the four looking directions (forward, backward, left and right) by 45 • with respect to the nadir camera. The workflow relies only upon open-source software: a developed tool to analyse image connectivity and Apero to orient the image block. The benefits of the connectivity tool are twofold: in terms of computational time and success of Bundle Block Adjustment. It exploits the georeferenced information provided by the Applanix system in constraining feature point extraction to relevant images only, and guides the concatenation of images during the relative orientation. Ultimately an absolute transformation is performed resulting in mean re-projection residuals equal to 0.6pix.
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Traditional image matching approaches often fail in processing Mars Global Surveyor stereo images because of low contrast and insufficient number of features. Reliable and precision matching tool is needed for precise Mars digital elevation model generation. This paper presents a hierarchical image matching approach. First, a number of well identified points are manually measured in a stereo pair. These measurements are input into a commercial digital photogrammetric tool as 'seed points' to generate more corresponding points. After that, the Delaunay triangulation network is formed for those seed points. The initial correspondence of an interest point on the other image is located by using the bilinear polynomial transformation whose coefficients are determined by six points closest to the interest point in the triangle. The conjugate point of the interest point in each triangle of the network is determined by using the parameters of the polynomial equation. In the next iteration, all the above generated corresponding points and the original seed points will be triangulated and the network will be densified in the same manner. This process repeats until the required point density is achieved. This initial prediction is then refined by using cross correlation and the least squares matching approaches. Based on this approach, we have matched stereo image pairs over selected landing sites for the Mars Exploration Rover mission. Assessment of image results suggests a matching consistency of better than one pixel. Presented in this paper are results of detailed matching quality evaluation and the generated digital elevation models.
Line matching of wide baseline images in an affine projection space
International Journal of Remote Sensing, 2019
Line matching plays an important role in vision localization and three-dimensional reconstruction of building structures. The conventional method of line matching is not effective for processing stereo images with wide baselines and large viewing angles. This paper proposes a line matching method in an affine projection space, aiming to solve the problem of change of viewing angles in aerial oblique images. Firstly, monocular image orientation can be performed through geometric structures of buildings. Secondly, according to the pose information of the camera, the affine projection matrix is obtained. The original image can be rectified as a conformal image based on this projection matrix, thereby reducing the difference in the viewing angle between images. Then, line matching is performed on the rectified images to get the matched line pairs. Finally, the inverse affine projection matrix is used to back-project the matched line pairs to the original images. The experimental results of five groups of aerial oblique images show that the matched line segments obtained by the proposed method are basically superior to those of the methods which are directly processed on the original image in terms of quantity, correctness, and efficiency.