Talking about satellite tracks

Aldozan

Member
Hi, I just want to share my experience in removing satellite tracks from frames.
I had two subframes with strong tracks:
track01.jpg
Track02.jpg

(already calibrated, CC, weighted, registered, drizzle and ln)

First Image Integration run applying Avereged Sigma Clipping (9 subframes) and default sigma values:
int_std.jpg
tracks are still well visible.

Best result obtained after tweaking sigma high to 1.5:
int_tweak.jpg
better, but still visible.

New approach, following this post: https://pixinsight.com/forum/index....zed-deviate-test-rejection.15698/#post-108259 and using Generalized Extreme Studentized Deviate with 0.77 ESD outliers and 0.79 ESD significance:
ESD.jpg
better, but tracks are still there and general quality of the image is deteriorated

Last chance, using Robust Chauvenet Rejection algorithm with RCR limit 0.8, range low 0, range high .98:
RCR.jpg
Finally tracks are out, but image has a lower SNR.

Conclusion: in this case at least, Yes you can remove tracks from your image, but you have to balance that with the lower SNR level of the integration. On a stack of 9 subframes my choice is to go with RCR, and when I run integration also with subframes from other session (46 images) I simply drop the two with the tracks.

This is my very, very little wisdom pill on satellite tracks removal. I would like to know other experience and learn from that.
 

Attachments

  • track01.jpg
    track01.jpg
    20.3 KB · Views: 64
  • Track02.jpg
    Track02.jpg
    30.8 KB · Views: 64
  • int_std.jpg
    int_std.jpg
    26.6 KB · Views: 66
  • int_tweak.jpg
    int_tweak.jpg
    26.8 KB · Views: 65
  • ESD.jpg
    ESD.jpg
    32.8 KB · Views: 66
  • RCR.jpg
    RCR.jpg
    32.5 KB · Views: 61
My experience is quite different: I never had any problem with satellite tracks rejection using the Generalized Extreme Studentized Deviate algorithm.
The difference with your experimentation is that I always had at least 40 images to integrate. I believe a sufficient number of frames is the key to a successful rejection and I would suggest not to drop any images for your 46 images session.
 
Thank you Nico. When, in your experience, do you put together subframes from different nights? If I understand well your point I should unite all the subframes and run a single Image Integration session. Or is it better to prepare several drizzle integration, one per night (4 in my case), and after that run Image Integration on the 4 drizzle integration frames?
 
Yes I believe it's much better to do a single Integration of all your calibrated (and registered) images and I think it will optimize the rejection. The workflow would be something like this:
-You calibrate your frames by nights (with a master flat for each nights)
-You register all calibrated files to a single reference frame
-You integrate all the registered files
-You do a drizzle integration

All these operations can be (and should be) done in a single instance of WBPP
 
Last edited:
Back
Top