Importing of PALSAR-2 level 1.5 HH and HV data

I have PALSAR-2 HH and HV geotiff files that need to be geocoded. An example of the format is: IMG-HH-ALOS2243123600-181123-WBDR1.5RUD.tif and of the summary file: summary.txt (2.1 KB)

I’m having trouble with the first step of importing the data using:
Import > Spaceborne Sensors > ALOS-2 - PALSAR-2 > Dual-Pol.
It asks for SAR Leader and Trailer Files, but I don’t have these within my scene retrieval files. I only have the geotiff, summary and LUT files, with the headers included in the geotiff file. The data can be retrieved in either CEOS or geotiff format, and I was wondering if there’s some way to work with the geotiff format within PolSARPro since I already have these files?

Some information about the two different file formats is included here:
https://www.pcigeomatics.com/geomatica-help/references/gdb_r/ALOS-2_PALSAR.html#ALOS-2_PALSAR

Radar polarimetry is based on the complex radar information (stored as i and q within the L1.1 products only). Therefore, you cannot do much with Level 1.5 data in PolSARpro

If you simply want to geocode /ortho-correct Level 1.5 data, you can use the GITASAR tool: https://www.researchgate.net/project/A-Tiny-Synthetic-Aperture-Radar-Image-Processing-Tool

Thanks @ABraun for your quick response. I’ll have a look at retrieving the L1.1 products instead.

I’ve also tried using GITASAR. I think this would be perfect for what I’m trying to do, but there only seems to be an import option for CEOS QuadPol. Do you know of a way to use geotiff files with it? And do you know if it will work with Dual-pol? (e.g. if I just enter image locations for the HH and HV fields)

You can try the StripMap option. I haven’t tried, but it could work out.

Thanks, I’ll retrieve the files in the CEOS format and then try that.

I know all of UAVSAR GRD products are geocode although i can’t export any products in POLSARPRO V6 in GEOTIFF formats and i dont know how geocode matrix T3 in POLSARPRO or ASF mapready. I want classifition with polarimetric decomposition and deep learning model