Dominique De Rauw

Wide band SAR sub-band splitting and inter-band coherence measurements

Range resolution of SAR images is determined by transmitted radar signal bandwidth. Most recent SAR sensors use wide band signals in order to achieve metric range resolution, whereas metric azimuth resolution can be achieved in spotlight mode. As an example, ENVISAT ASAR sensor uses a 15-MHz bandwidth chirp whereas TerraSAR-X spotlight mode uses signals having a 150-MHz bandwidth leading to a potentially 10 times higher resolution. One can also take advantage of wide band to split the full band into sub-bands and generate several lower resolution images from a single acquisition, each being centred on slightly different frequencies. These sub-images can then be used in a classical interferometric process to measure inter-band coherence of a given scene. This inter-band coherence reveals scatterers keeping a stable-phase behaviour along with frequency shift. A simple coherence model derived from Zebker model for randomly distributed surface scatterers is proposed. Examples are presented, showing that scatterers can have a behaviour that deviates from the model, leading to a new information channel.

Split-Band Interferometry-Assisted Phase Unwrapping for the Phase Ambiguities Correction

Split-Band Interferometry (SBInSAR) exploits the large range bandwidth of the new generation of synthetic aperture radar (SAR) sensors to process images at subrange bandwidth. Its application to an interferometric pair leads to several lower resolution interferograms of the same scene with slightly shifted central frequencies. When SBInSAR is applied to frequency-persistent scatterers, the linear trend of the phase through the stack of interferograms can be used to perform absolute and spatially independent phase unwrapping. While the height computation has been the main concern of studies on SBInSAR so far, we propose instead to use it to assist conventional phase unwrapping. During phase unwrapping, phase ambiguities are introduced when parts of the interferogram are separately unwrapped. The proposed method reduces the phase ambiguities so that the phase can be connected between separately unwrapped regions. The approach is tested on a pair of TerraSAR-X spotlight images of Copahue volcano, Argentina. In this framework, we propose two new criteria for the frequency-persistent scatterers detection, based respectively on the standard deviation of the slope of the linear regression and on the phase variance stability, and we compare them to the multifrequency phase error. Both new criteria appear to be more suited to our approach than the multifrequency phase error. We validate the SBInSAR-assisted phase unwrapping method by artificially splitting a continuous phase region into disconnected subzones. Despite the decorrelation and the steep topography affecting the volcanic test region, the expected phase ambiguities are successfully recovered whatever the chosen criterion to detect the frequency-persistent scatterers. Comparing the aspect ratio of the distributions of the computed phase ambiguities, the analysis shows that the phase variance stability is the most efficient criterion to select stable targets and the slope standard deviation gives satisfactory results.

Automated Phase Map Referencing Against Historic Map Data

Roger M. Groves, Dominique Derauw, Cedric Thizy, Igor Alexeenko, Wolfgang Osten, Marc Georges, Vivi Tornari Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2600 GB, Delft, The Netherlands; ITO Institut fur Technische Optik, Universitat Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany; Centre Spatial de Liege – Universite de Liege, Liege Science Park, B-4031 Angleur, Belgium; Foundation for Research and Technology-Hellas, Institute of Electronic Structure and Laser, Vassilika Vouton, Voutes, 71110 Heraklion-Crete, Greece

Spectral coherence applied to vessel tracking

Multi-Chromatic or spectral Analysis (MCA) of SAR images consists in splitting wide band SAR signals into sub-bands to generate several lower resolution images from a single acquisition. This splitting allows performing a spectral analysis of observed scatterers. Spectral coherence is derived by computing the coherence between sub-images issued from a single SAR acquisition. It was shown that in the presence of a random distribution of surface scatterers, spectral coherence is proportional to sub-band intersection of sub-images. This model is fully verified when observing spectral coherence on open seas areas. If the scatterers distribution departs from this distribution, like for manmade structures, spectral coherence may be preserved to a certain degree. We investigated the spectral coherence to perform vessel detection on a sea background by using spotlight images acquired on the Venice Lagoon. Sea background tends to lead to very low spectral coherence while this latter is preserved on the targeted vessels, even for very small ones. A first analysis shows that all vessels observable in intensity images are easily detected in the spectral coherence images which can be used as a complementary information channel to constrain vessel detection.