3D mapping from space?

Prof Dr Armin Gruen, Dr Kirsten Wolff

Geo-referencing

Today geo-referencing from satellite images is well understood and controlled. It is the least problem we encounter in 3D topo-mapping. In previous projects we have collected a lot of experiences in geo-referencing. We have used SPOT-5, ALOS/PRISM, Cartosat-1, IKONOS and Quickbird images over different testfields worldwide (Germany, Italy, Japan, South Africa, Switzerland, Turkey, Vietnam). We have developed the software SAT-PP (Satellite Image Precision Processing), which includes several strict models for the most important sensors and also the related Rational Polynomial Function (RPF) approaches. With this software we have obtained consistent results in the subpixel domain, both for planimetry and height and for all sensors, using few (2-5) GCPs only. We could show that RPCs usually provide good relative orientation, while the absolute orientation has substantial systematic errors. These kinds of errors depend on the satellite/sensor. In the best case they represent just a bias (shift in coordinates), in other cases we diagnosed higher order terms. In any case the distortions can be removed with the concept of biascorrected RPCs and the use of 1-3 GCPs.

DTM generation

DTM generation is a key issue in topomapping. If produced in manual mode this does not constitute a problem, it only needs time – a lot of time. Therefore we turn towards automated DSM generation by image matching. Image matching – in its essence – is still an unsolved problem. With our software SAT-PP, which includes an advanced matching module, we obtain height accuracies between 1 and 5 pixels from high-resolution satellite images, depending on the type of terrain, land cover, image texture and image quality. While RMS errors in such tests show good results we must note that in all these cases substantial blunders (10 times the RMSE and more) still exist in the data. This is not acceptable. This can only be solved by substantial and time-consuming postediting of the DSM or by efforts to better understand the reasons for such blunders in image matching, with the aim to get rid of them. Therefore the avoidance and/or detection of blunders in the automatically generated DSM is a critical point for future research and development.

The next problem we are faced with is the reduction of the DSM, produced by the image matcher, to the DTM, as represented in the landscape model. Although there are some attempts available to automatically perform the reduction, the results are not convincing, because these algorithms are purely based on geometrical considerations. What is needed however is an image or point cloud interpretation approach which lets us understand what kind of object we are dealing with in the reduction process at a particular location.

Object extraction

We have experience with automated and semi-automated feature and object extraction, primarily in 3D city modelling and 3D road extraction. In 3D city modelling we use our semiautomated procedure CyberCity Modeler (CC-Modeler) for building extraction. With some examples derived from IKONOS and Quickbird images we could show to what extent and at which resolution these objects can be modelled from satellite imagery.

In road extraction we have developed “LSB-Snakes” (Least Squares B-Spline Snakes), a semi-automated technique which allows us to model roads in 3D.

In addition, the well-known technique of monoplotting can be used for object extraction (Fraser et al., 2008). This procedure works usually well, but with limited accuracy, depending on the quality of the underlying DTM.

In the following test the measurements of the topographic features (buildings, forests, streets, lakes, single trees and contour lines) for the map scale 1:25,000 were done by an experienced stereo operator of our group. For the other special topographic features we got support from an experienced topographer from swisstopo.

Pilot mapping project Thun

A key issue in mapping is the interpretability of images of a particular resolution. Currently this topic is in the center of our interest, because we believe there are misconceptions on this issue. We are conducting investigations to find out which objects can be extracted under which geometrical resolutions. Here we present some preliminary results. For more details please see Gruen and Wolff, 2008.

For a first test we selected the test area of Thun, Switzerland. This is a fairly flat urban zone which is composed of areas with single family and apartment houses with parks, an industrial area, a military airport and forest areas. This area contains many of the important features of a topographic map. For the manual drawing of contour lines we extended the area to a hilly region, including forest and open areas without any substantial buildings.

The aim of this investigation was to analyze the possibilities to identify and map buildings, roads and other individual features for a 1:25,000 topographic map by using high resolution satellite images data. For such a mapping scale we assumed that a GSD of 1m or even higher is required. In conventional mapping and map updating aerial images of scale 1:30,000 are used (which corresponds to a GSD of about 0.5 m). For our test area Thun two IKONOS panchromatic stereo images (December 2003, GSD 1m) were available.