Radiometric and geometric characteristics of Pleiades images
Pleiades images are distributed with 50 cm ground sampling distance (GSD) even if the physical resolution for nadir images is just 70 cm. By theory this should influence the effective GSD determined by means of point spread function at image edges. Nevertheless by edge enhancement the effective GSD can be improved, but this should cause enlarged image noise. Again image noise can be reduced by image restoration. Finally even optimized image restoration cannot improve the image information from 70 cm to 50 cm without loss of details, requiring a comparison of Pleiades image details with other very high resolution space images. The image noise has been determined by analysis of the whole images for any sub-area with 5 pixels times 5 pixels. Based on the standard deviation of grey values in the small sub-areas the image noise has been determined by frequency analysis. This leads to realistic results, checked by test targets. On the other hand the visual determination of image noise based on apparently homogenous sub-areas results in too high values because the human eye is not able to identify small grey value differences – it is limited to just approximately 40 grey value steps over the available gray value range, so small difference in grey values cannot be seen, enlarging results of a manual noise determination. A tri-stereo combination of Pleiades 1A in a mountainous, but partially urban, area has been analyzed and compared with images of the same area from WorldView-1, QuickBird and IKONOS. The image restoration of the Pleiades images is very good, so the effective image resolution resulted in a factor 1.0, meaning that the effective resolution corresponds to the nominal resolution of 50 cm. This does not correspond to the physical resolution of 70 cm, but by edge enhancement the steepness of the grey value profile across the edge can be enlarged, reducing the width of the point spread function. Without additional filtering edge enhancement enlarges the image noise, but the average image noise of approximately 1.0 grey values related to 8 bit images is very small, not indicating the edge enhancement and the down sampling of the GSD from 70 cm to 50 cm. So the direct comparison with the other images has to give the answer if the image quality of Pleiades images is on similar level as corresponding to the nominal resolution. As expected with the image geometry there is no problem. This is the case for all used space images in the test area, where the point identification limits the accuracy of the scene orientation.
Analysis of height models based on Kompsat-3 images
Abstract. Height models are basic information required for disaster Management. Not in any case satisfying and actual height models are available, but they can be generated by satellite stereo pairs being more precise as InSAR. The Korean Kompsat-3 has a ground sampling distance of 0.71m. A stereo combination covering the main part of Istanbul has been used for the generation of height models. Kompsat-3 images are available as L1R images, corresponding close to the original image geometry, and as L1G, being projected to the SRTM 3 arcsec height model. For use of Semi Global Matching quasi epipolar images are required. They can be produced by just rotating the L1G-images to the stereo base, while with L1R-images requires at first a projection to a constant height level. The projection of L1G to the SRTM height models leads to height differences against the SRTM heights. The orientation of the L1R images with 71 ground control points (GCP) was possible in X and Y with 0.6 GSD and in Z with 1.1 GSD, while with L1G images only 1.2 GSD respectively 2.9 GSD have been reached. A standard deviation of 0.6 GSD for X and Y and 1.1 GSD for Z is satisfying and a usual accuracy for satellite images. A comparison of the generated height model based on the L1G-images with airborne LiDAR data (ALS) showed clear local systematic height errors of the height model based on L1G-images which could not be seen with L1R-images. The area based least squares matching leads to good results in open areas while in build up areas no accurate building determination is possible. Here SGM has a clear advantage with accurate roof structures corresponding to the 0.71 m GSD. For the relative accuracy, that means the building height and the roof structure, it does not matter if L1G or L1R images are used.
Comparison of Matching Algorithms for DSM Generation in Urban Areas from Ikonos Imagery
The extraction of the third dimension from remote sensing data is a well known technique. Since in a number of countries aerial images and laser scanner data are unavailable, expensive or classified, stereoscopic high-resolution optical satellite images provide a viable alternative for generating digital surface and digital terrain models. Especially the automatic extraction of highly accurate 3D surface models in urban areas is still a very complicated task due to occlusions, large differences in height and the variety of objects and surface material. In this paper an analysis and a visual and quantitative comparison of three different matching algorithms for generating urban DSMs based on very high-resolution satellite images is presented. The three algorithms are least squares matching (LSM) in a region growing fashion, dynamic programming (DP) and semiglobal matching (SGM). The characteristics of the three algorithms as applied to four different Ikonos stereo pairs with a ground sampling distance of 1 m are shown. The following results were obtained: visually, in the LSM results the shape of the buildings is considerably smoothed. While in the DP results the building shape is sharper, only little detail is visible on the building roofs, and streaking along the epipolar lines causes problems. With SGM more details can be extracted and the results visually have the best quality. Based on reference data for the different test sites, the standard deviation of the building heights determined by LSM and DP is in the range of one pixel or slightly better, while it is in the range of half a pixel for SCM.