High‐resolution digital elevation models derived from Viking Orbiter images: Method and comparison with Mars Orbiter Laser Altimeter Data

Abstract

The purpose of this paper is to present a method to derive local and high‐resolution digital elevation models (DEM) of Mars from Viking Orbiter images. We focus on two aspects that appear to be new in photogrammetry applied to Mars: the correlation method and the relative orientation method based on the analysis of the coplaneity. We demonstrate that a DEM from two Viking Orbiter images selected according to the criteria of parallax height ratio, resolution, and illumination conditions can be obtained with a better spatial lateral resolution than the interpolated Mars Orbiter Laser Altimeter (MOLA) data. Two images on the Deuteronilus Mensae area satisfy these criteria. The algorithm of reconstruction of the topography is divided into two steps. First, a correlation method at subpixel level based on Fourier transform has been adapted to extract image location of homologous points. As initial camera positions and angles are known with poor accuracy, an adjustment is then carried out by the method of relative orientation which requires no absolute control points and takes advantage of the subpixel accuracy of our matching algorithm. Finally, the DEM is projected into a Martian reference frame and registered to MOLA data for validation. The spatial resolution depends on the roughness of the area and is locally close to the distance between individual MOLA footprints along one profile (300 m), as demonstrated from the comparison with MOLA profiles. These first results of high‐resolution DEM from Viking Orbiter images should be used with MOLA data for many geological studies that require high‐resolution topographic data.

Potential and Limitation of SPOT-5 Ortho-Image Correlation to Investigate the Cinematics of Landslides: The Example of “Mare à Poule d’Eau” (Réunion, France)

“…The mathematical principles of image correlation have been extensively presented in others studies [17,18] and are briefly outlined. The purpose of image correlation is to detect similar pixel organizations or objects on two images acquired in different conditions in order to measure the shift of the homologous pixels between two images.…”

“…A homologous window of the same size is searched on the other image that will maximize a correlation function. The process can be refined to obtain a precision that reaches one-tenth of a pixel in the ideal case [17,18] which is 0.25 m for a SPOT-5 image with a 2.5 m resolution. Various correlation functions can be used that can be statistical or defined in the frequency domain after the decomposition of the image signal in its frequency components.…”

“…On two images acquired at different times and ortho-rectified (geometrically corrected from the topographic effect), homologous points are detected by correlation methods with a theoretical sensitivity of one-tenth of a pixel [17,18].The shift detected between the homologous points on the two images corresponds to the displacement that occurred between the acquisition of the two images. The correlation of ortho-rectified optical images has been applied to measure the co-seismic displacement fields [19,20], the displacement of glaciers [21,22], of landslides [23], and of dunes [24].…”

DRELIO : Un drone hélicoptère pour le suivi des zones littorales

“…L'Institut Universitaire Européen de la Mer (IUEM) et l'Université Lyon 1 ont développé un hélicoptère photogrammétrique télécommandé : DRELIO (DELACOURT et al, 2009 (d'après KRAUS et al, 1979 ;JENSEN, 2000 ;CASSON et al, 2003) ( Les deux images du couple stéréoscopique sont ensuite projetées dans une géométrie proche "pseudo-orthorectifiée", se prêtant à une comparaison par fenêtre glissante. L'étape suivante consiste, par corrélation d'images, à déterminer la position de chaque point de la première image pseudo-orthorectifiée et la position du point correspondant sur la seconde image pseudo-orthorectifiée (BARATOUX et al, 2001 ;DELACOURT et al, 2004). On obtient finalement trois matrices contenant pour chaque point le coefficient de corrélation et les décalages respectifs selon x, et y entre les deux images pseudo-orthorectifiées.…”

Contribution of multi-temporal remote sensing images to characterize landslide slip surface ‒ Application to the La Clapière landslide (France)

“…Finally, DEMs are calculated from camera parameters and homologous point positions. A least mean square inversion is used to improve the intersection of lines of sight of the two cameras (Baratoux et al, 2001;Casson et al, 2003). After this step, the distance between the lines of sight of the two cameras has to be null on each DEM point.…”