SARPROZ ©

Hi,

a journal paper that described the core algorithms in Sarproz:

D. Perissin and T. and Wang, “Repeat-Pass SAR Interferometry with Partially Coherent Targets,” IEEE Transactions on Geoscience and Remote Sensing, vol. Volume 50, p. Pages 271–280, 2012.

a confe…[Read more]

you are right that a low number of images implies low robustness in the estimation of the coherence and a bias towards high values. That said, numbers are relative and dependent also on other factors. so, it is difficult to tell you what number to use without looking at your dataset and at your results in detail. play with the numbers and observe…[Read more]

Thanks, your help was very useful!

Okay! I understand much better.
And for the non linear weighning part, what would you recommend? I understand that the lower the number of images, the more coherence is required to trust the data, so m=0.8 would be a good value when it comes to few images, is that right?

Thanks!

Hi Paulina,
there are multiple things to consider in your case. An exhaustive answer to your question would require to discuss about various topics. You might want to take a Sarproz course to get more experience (in case, contact courses@sarproz.com). For the moment let me tell you that you should consider the APS estimation as an independent…[Read more]

APS is long wave length signal. So 0.7 seems fine spatial diatribution for aps. In sparse point protssing u may use lower ASI 0.6 for exaple

Hi Jaroslav,
at the moment, Sarproz saves the 25 reference points in a temporary variable. That variable can be accessed if you run Sarproz in Matlab using the pcodes version. You can access it if you type “fast” in the command prompt when Sarproz is running and the APS module is open and the function for automatic selection of reference points…[Read more]