State of the art for SAR Co-Registration

Hello everyone,

I am sorry if this post could seem too “basic” for most of you, but I am approaching to the “world” of Geospatial data analysis very recently.
For the moment, I am concentrating on the analysis of Sentinel-1 data and my particular interest is the Co-Registration of SAR images to use them for Interferometry.

Thus, I am trying to trace the state of the art for the Co-Registration methodologies and I started this topic trying to clear my mind about the topic and, possibly, to share recent studies/researches about it.

Till now, I understood that there are 2 main possibilities to Co-Register SAR images:

  1. With orbit information (+ DEM)
  2. Cross-Correlation

The first approach performs pixel-to-pixel co-registration and basically maps the SAR coordinates to Cartesian coordinates. It is what is typically done with a standard (?) pipeline in SNAP that more or less can be summarized in the following:

TOPS Split → Apply Orbit → TOPSAR Back Geocoding + ESD

However, the results of this approach strictly depend on the precision of orbit files. As far as I understood, the main problem here is that the precise orbits are available after 20 days for SAR products. So if you wish to analyse data acquired in the last 20 days, you need to use restituted orbits and it is very likely that this approach will not produce good results (maybe here I am speaking too broadly since the goodness of results may depend on the application).

On the other hand, the second approach does not rely on Orbit information but it should use Cross-Correlation to estimate the offset between the images, then you can perform a rigid image transformation (parameters should be estimated, too) to Co-Register the products.
I did not explore further this approach till now, and I did not even understand whether or not there are Operators in SNAP that perform this type of co-registration too.

Both of these approaches need an ESD correction (Improving co-registration accuracy of interferometric SAR images using spectral diversity by R. Scheiber and A. Moreira) that should estimate both the Azimuth and Range shifts comparing the two phases of the images to further correct the process of Co-Registering them.

Exception made for these 2 general approaches (and all their possible variations based on data knowledge and application), it is not clear (to me at least) if there are other deep-rooted approaches to handle the task.

I would really appreciate if you could comment this post to underly all the “errors” in my considerations above, but also if you could suggest me some interesting reading to further understand the problem and its newest possible solutions.



For the Sentinel-1 satellites (1A and 1B), the restituted orbits are very accurate. In practice, there is usually no significant difference between the RESORB and POEORB orbits. When we are doing disaster response InSAR processing with Sentinel-1 data, we always use the RESORB orbits and have not had any problems with the coregistration.

I believe that SNAP has functions to do the amplitude cross-correlation method for SAR images, as this is necessary for older SAR satellites that did not have such accurate orbits and for L-band SAR where the ionosphere can affect coregistration. I don’t know if those functions work on Sentinel-1 TOPS data. Cross-correlation methods are good to about 1/10 or 1/20 of the pixel spacing but can be averaged over a whole scene to get better average shift for the scene.

The ESD (enhanced spectral diversity) method only estimates a very precise along-track or azimuth shift from the phase of burst overlap interferograms, to an accuracy of about 1/1000 of an azimuth pixel.

We found that the “extra” shift that ESD estimates for Sentinel-1 pairs, after the orbit-based coregistration is completed, is usually due to ionospheric effects. You can read our paper on using the range split-spectrum method to estimate the ionosphere and correct for range and azimuth shifts.
Liang, C., P. Agram, M. Simons, and E. J. Fielding (2019). Ionospheric Correction of InSAR Time Series Analysis of C-band Sentinel-1 TOPS Data, IEEE Transactions on Geoscience and Remote Sensing 57, no. 9**,** 6755-6773, doi:10.1109/TGRS.2019.2908494.

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