Thanks to satellite and airborne imagery, 3D reconstruction of earth surface is possible. Optical imagery exploits stereoscopic acquisitions and photogrammetry to retrieve 3D surface whereas interferometry is used for radar imagery. These techniques are complementary. Radar images allow the retrieval of fine metallic objects such as pylons. Optical imagery is more robust but such fine details cannot be preserved due to smoothing. An objective of the post-doctorate is to detect such fine objects.
The complementarity between 3D cloud points retrieved from satellite optical imagery and satellite and airborne radar imagery should lead to a 3D product including objects principally detected by radar and surface reconstruction derived from optical imagery.
The post-doctorate will begin with a state of the art review on 3D reconstruction by optical and radar imagery as well as cloud points fusion. Different 3D reconstruction processing chains should be used on airborne and satellite images. A precise registration algorithm and fusion algorithm on cloud points should be developed, enabling the detection of points detected only by radar. For this step, Deep Learning techniques could be useful. The results will be compared to 3D very high resolution acquired by Lidar to quantify the results quality of the proposed algorithm.
This post-doctorate will take place in labs specialized in satellite and radar image processing through a collaboration between CEA-DAM and Onera.