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Recent developments of digital cameras in terms of the size of a Charged Coupled Device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) arrays, as well as reduced costs, are leading to their applications in traditional and new photogrammetric, surveying, and mapping functions. Such cameras require careful calibration to determine their metric characteristics, as defined by the Interior Orientation Parameters (IOP), which are essential for any photogrammetric activity. Moreover, the stability of the estimated IOP of these cameras over short and long time periods has to be analyzed and quantified. This paper outlines the incorporation of straight lines in a bundle adjustment procedure for calibrating off-the-shelf/low-cost digital cameras. A framework for automatic extraction of the straight lines in the images is also presented and tested. In addition, the research introduces new approaches for testing the camera stability, where the degree of similarity between reconstructed bundles using two sets of IOP is quantitatively evaluated. Experimental results with real data proved the feasibility of the line-based selfcalibration approach. Analysis of the estimated IOP from various calibration sessions over long time periods revealed the stability of the implemented camera.

Many photogrammetric and GIS applications, such as city modelling, change detection and object recognition, deal with surfaces. Change detection involves looking for differences between two surface models that are obtained from different sensors, for example an optical sensor and a laser scanner, or by the same sensor at different epochs. Surfaces obtained through a sampling process may also have to be compared for future processing (for example transformation parameter estimation and change detection). Surface matching is therefore an essential task in these applications. The matching of surfaces involves two steps. The first step deals with finding the correspondences between two surface points and/or patches. The second step requires the determination of transformation parameters between the two surfaces. However, since most surfaces consist of randomly distributed discrete points and may have different reference systems, finding the correspondences cannot be achieved without knowing the transformation parameters between the two surfaces. Conversely, deriving the transformation parameters requires the knowledge of the correspondence between the two point sets. The suggested approach for surface matching deals with randomly distributed data sets without the need for error prone interpolation and requires no point-to-point correspondence between the two surfaces under consideration. This research simultaneously solves for the correspondence and the transformation parameters using a Modified Iterated Hough Transform for robust parameter estimation. Several experiments are conducted to prove the feasibility and the robustness of the suggested approach, even when a high percentage of change exists.

Increased use of digital imagery has facilitated the opportunity to use features, in addition to points, in photogrammetric applications. Straight lines are often present in object space, and prior research has focused on incorporating straight-line constraints into bundle adjustment for frame imagery. In the research reported in this paper, object-space straight lines are used in a bundle adjustment with self-calibration. The perspective projection of straight lines in the object space produces straight lines in the image space in the absence of distortions. Any deviations from straightness in the image space are attributed to various distortion sources, such as radial and decentric lens distortions. Before incorporating straight lines into a bundle adjustment with self-calibration, the representation and perspective transformation of straight lines between image space and object space should be addressed. In this investigation, images of straight lines are represented as a sequence of points along the image line. Also, two points along the object-space straight line are used to represent that line. The perspective relationship between image-and object-space lines is incorporated in a mathematical constraint. The underlying principle in this constraint is that the vector from the perspective centre to an image point on a straight-line feature lies on the plane defined by the perspective centre and the two object points defining the straight line. This constraint has been embedded in a software application for bundle adjustment with self-calibration that can incorporate point as well as straight-line features. Experiments with simulated and real data have proved the feasibility and the efficiency of the algorithm proposed. K: bundle adjustment, self-calibration, straight lines I M   are based on the use of distinct points. These points are often obtained from measurements in an analogue or digital

Resampled imagery according to epipolar geometry, usually denoted as normalised imagery, is characterised by having conjugate points along the same row (or column). Such a characteristic makes normalised imagery an important prerequisite for many photogrammetric activities such as image matching, automatic aerial triangulation, automatic digital elevation model and orthophoto generation, and stereo viewing. The normalisation process requires having the input imagery in a digital format, which can be obtained by scanning analogue photographs or by direct use of digital cameras. To reduce the time gap between the data acquisition and product delivery, many small‐scale mapping projects now rely on digital cameras. Digital frame cameras still, in general, provide imagery with geometric resolution and ground coverage inferior to scanned images from analogue cameras. Linear array scanners (line cameras) have therefore emerged as a possible alternative to digital frame cameras, especially for high‐resolution space‐borne imaging, with performance comparable to that of analogue frame cameras. The normalisation process of frame images is a well‐established and straight forward procedure. On the other hand, the normalisation process of linear array scanner scenes is not as straightforward and is sometimes mysterious. For example, providers of space‐borne imagery furnish normalised line scanner imagery while the user community is not aware of the underlying process. This paper presents a comprehensive analysis of the epipolar geometry in linear array scanner scenes. Special emphasis is directed towards scanners moving with constant velocity and attitude since such a trajectory closely resembles the imaging geometry of the majority of current space‐borne scanners. The research presented highlights the factors affecting the shape of the resulting epipolar lines such as the stereo coverage configuration and the geometric specifications of the imaging system. In addition, the paper outlines a comparative analysis of the normalisation process for frame and line cameras. The presented concepts are verified through experimental results with synthetic data.

Increased use of digital imagery has facilitated the opportunity to use features, in addition to points, in photogrammetric applications. Straight lines are often present in object space, and prior research has focused on incorporating straight‐line constraints into bundle adjustment for frame imagery. In the research reported in this paper, object‐space straight lines aw used in a bundle adjustment with self‐calibration. The perspective projection of straight lines in the object space produces straight lines in the image space in the absence of distortions. Any deviations from straightness in the image space are attributed to various distortion sources, such as radial and decentric lens distortions. Before incorporating straight lines into a bundle adjustment with self‐calibration, the representation and perspective transformation of straight lines between image space and object space should be addressed. In this investigation. images of straight lines are represented as a sequence of points along the image line. Also, two points along the object‐space straight line are used to represent that line. The perspective relationship between image‐ and object‐space lines is incorporated in a mathematical constraint. The underlying principle in this constraint is that the vector from the perspective centre to an image point on a straight‐line feature lies on the plane defined by the perspective centre and the two object points defining the straight line. This constraint has been embedded in a software application for bundle adjustment with self‐calibration that can incorporate point as well as straight‐line features. Experiments with simulated and real data have proved the feasibility and the eficiency of the algorithm proposed. Résumé Le développement de l'imagerie numérique a fourni l'occasion de recourir davantage aux détails des objets, et non pas seulement aux points qui les constituent, duns toute application photogrammétrique. C'est ainsi que les objets présentent souvent des lignes droites qu'il était tentant, dans une recherche antérieure, d'introduire pour contraindre la compensation par faisceaux d'imageries photographiques. On présente dans cet article cette recherche où les lignes droites de l'espace objet sont utilisées dans une compensation par faisceaux avec auto‐étalonnage. En l'absence de distorsions, la projection de lignes droites de l'espace objet dans l'espace image s'opère également sous forme de lignes droites. Tout écart à une droite sur l'image peut done être attribuéà toutes sortes de distorsions, comme la distorsion radiale ou cello due au décentrement de l'objectif. Avant d'utiliser ces lignes droites dans une compensation par faisceaux avec auto‐étalonnage, il faut efectuer la représentation et la transformation perspective des lignes droites entre les espaces objet et image. Dans cette démarche, on considère les images des lignes droites comme constituées d'une suite de points jalonnant cette image tandis que duns l'espace objet cette ligne droite n'est définie que par deux points...

Image resampling according to epipolar geometry is an important prerequisite for a variety of photogrammetric tasks. Established procedures for resampling frame images according to epipolar geometry are not suitable for scenes captured by line cameras. In this paper, the mathematical model describing epipolar lines in scenes captured by line cameras moving with constant velocity and attitude is established and analyzed. The choice of this trajectory is motivated by the fact that many line cameras can be assumed to follow such a flight path during the short duration of a scene capture (especially when considering space‐borne imaging platforms). Experimental results from synthetic along‐track and across‐track stereo‐scenes are presented. For these scenes, the deviations of the resulting epipolar lines from straightness, as the camera's angular field of view decreases, are quantified and presented.