<title>SPOT stereo matching for DTM generation</title>

Abstract

This paper presents a matching algorithm for automatic DTM generation from SPOT images that provides dense, accurate and reliable results and attacks the problem of radiometric differences between the images. The proposed algorithm is based on a modified version of the Multiphoto Geometrically Constrained Matching (MPGC). It is the first algorithm that explicitly uses the SPOT geometry in matching, restricting thus the search space in one dimension, and simultaneously providing pixel and object coordinates. This leads to an increase in reliability, and to reduction and easier detection of blunders. The sensor modelling is based on Kratky's polynomial mapping functions to transform between the image spaces of stereopairs. With their help epipolar lines that are practically straight can be determined and the search is constrained along these lines. The polynomial functions can also provide approximate values, which are further refined by the use of an image pyramid.Radiometric differences are strongly reduced by performing matching not in the grey level but in gradient magnitude images. Thus, practically only the information in stripes along the edges is used for matching. Edges that exist in only one image can be detected by subtracting quasi registered images in the upper levels of an image pyramid. The points to be matched are selected by an interest operator. Gross errors can be detected by statistical analysis of criteria that are provided by the algorithm and by a robust analysis of the heights within local neighbourhoods.The results of an extensive test using a stereo SPOT model over Switzerland will be reported. Matching with different options and the qualitative comparison of the results based on thirty thousand check points will be presented.

Evaluation of Stereo Matching Costs on Images with Radiometric Differences

“…Alternatively, an offset change can also be reduced by filtering the images before matching using a mean filter, computing a gradient magnitude image (i.e. first derivative) [8] or Laplacian of Gaussian (i.e. smoothed second derivative) [9], [10].…”

Monocular robot navigation using invariant natural features

“…The algorithm finds the corresponding point in the neighborhood of the selected point in the search images by minimizing the sum of the squares of the differences between the grey levels in these patches. The process starts from a seed point, shifts the point in the search images and estimates the parameters of transformation from these observed gray level differences by a least squares adjustment [17].…”

Generación de Modelos de Elevación Digital (DEM) a partir del análisis fotogramétrico utilizando imágenes CARTA-2005

“…Otro inconveniente de los vuelos aerotransportados tiene que ver con los diferentes grados y ángulos de iluminación que se den durante el vuelo, el cual puede durar días o incluso meses. Esto hace que se produzcan diferencias radiológicas en las imágenes, dadas las diferentes fechas de adquisición, que resultan difíciles de compensar en el proceso de análisis fotogramétrico (Baltsavias & Stallmann, 1993). Como consecuencia de esto, muchas veces se generan zonas de "redundancia" de información o datos que resultan irrelevantes.…”