ALBERT

Notes: 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.

Notes: 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.

Notes: 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.

Notes: Image registration aims at combining imagery from multiple sensors to achieve higher accuracy and derive more information than that obtained from a single sensor. The enormous increase in the volume of remotely sensed data that is being acquired by an ever-growing number of earth observation satellites mandates the development of accurate, robust, and automated registration procedures. An effective automatic image registration has to deal with four issues: registration primitives, transformation function, similarity measure, and matching strategy. This paper introduces a new approach for automatic image registration using linear features as the registration primitives. Linear features have been chosen because they can be reliably extracted from imagery with significantly different geometric and radiometric properties. The modified iterated Hough transform (MIHT), which manipulates the registration primitives and similarity measure, is used as the matching strategy for automatically deriving an estimate of the parameters involved in the transformation function as well as the correspondence between conjugate primitives. The MIHT procedure follows an optimal sequence for parameter estimation that takes into account the contribution of linear features with different orientations at various locations within the imagery towards the estimation of the transformation parameters in question. Experimental results using real data proved the feasibility and robustness of the suggested approach.

Notes: 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.〈section xml:id="abs1-2"〉〈title type="main"〉RésuméLe 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 seulement. La relation de perspective qui relie les droites des espaces objet et image est alors introduite comme contrainte mathématique. Le principe de base de cette contrainte est que le vecteur issu du centre perspectif vers un point image d'une ligne droite de l'objet appartient au plan défini par re centre perspectif et les deux points retenus dans la définition de cette droite. On a incorporé cette contrainte dans un logiciel appliquéà la compensation par faisceaux avec auto-étalonnage prenant en compte les points ainsi que les éléments en ligne droite de l'objet. Des essais avec des données simulées puis réelles ont montré la faisabilité et l'efficacité de l'algorithme proposé.〈section xml:id="abs1-3"〉〈title type="main"〉ZusummenfussungDurch die zunehmende Nutzung digitaler Bilder wurde die Möglichkeit geschaffen, neben Punkten auch Objektmerkmale in photogrammetrischen Anwendungen zu nutzen. Oftmals finden sich Geraden in Objektraum, und frühere Forschung hat sich darauf konzentriert, Linienbedingungen in die Bündelausgleichung für Flächenkameras zu entwickeln. In den hier vorgestellten Forschungen werden Geraden im Objektraum in einer Bündelausgleichung mit Selbstkalibrierung eingesetzt. Wenn keine Verzeichnungen vorliegen, werden bei einer perspektiven Abbildung Geraden im Objektraum in Geraden im Bildraum abgebildet. Jegliche Abweichung von einer Gerden m Bildraum kann mit verschiedenen Ursachen für Verzeichnung in Verbindung gebracht werden, wie zum Beispiel radiale order asymmetrische Objektivverzeichung. Bevor Gerden in die Bündelausgleichung mit Selbstakalibrierung eingehen können, sollte die Repräsentation und die perspektive Transformation der Gerden zwischen Bild- und Objektraum geklärt werden. In dieser Untersuchung werden die Abbildungen der Geraden als eine Sequenz von Punkten entlang einer Bildlinie dargestellt. Zwei Punkte entlang der Objektgeraden werden genutzt, um diese darzustellen. Die perspektive Beziehung zwischen Bild- und Objektgeraden wird mit Hilfe einer mathematischen Bedingung formuliert. Das Prinzip, das dieser Beziehung zugrunde liegt, geht davon aus, dass ein Vektor vom Projektionszentrum zu einem Bildpunkt auf einer Geraden auf einer Ebene liegt, die durch das Projektionszentrum und die zwei Objektpunkte, die die Gerade definieren, bestimmt wird. Diese Bedingung wurde in ein Anwendungsprogramm zur Bündelausgleichung mit Selbstakalibrierung eingbaut, das sowohl Punkt- als auch Geradenmerkmale verarbeiten kann. Experimente mit simulierten und echten Datensätzen belegen die Anwendbarkeit und die Effizienz des vorgeschlagenen Algorithmus.