Help building normalized change detection workflow

Hello all,

I am trying to build a workflow for normalized change detection between multiple image pairs. I am using 30 S1 images of type GRD and using the following steps:

  1. Import images
  2. Subset to AOI
  3. Calibrate to Beta 0
  4. Dem Assisted Corregistration
  5. Band math for normalized change detection (A2 - A1)/(A2 + A1)

I have a few questions:
Is beta nought the recommended calibration for this procedure?
Can I automatically split polarization bands in S1 products? I don’t want to stack VV with VH pol in the same product…
When should I apply multi temporal speckle filtering? I get a lot of noise in change detection results.

Sigma0 is the more advanced measure because it integrates the incidence angle in your image.

There is a “select band” tool which allows you to only work with VV in the gpt

Multi-temporal filtering is not a good idea for change detections because you might erase the pixels which underwent an actual change. Furthermore, the multi-temporal filter only works with lots of images in the stack. Two images will are not enough to discriminate constant changes from random backscatter variations.
I would rather recommend filtering before the band differencing in the band maths.

I am using a 30 image stack (29 pairs). So you recommend single product filtering on each image?

1 Like

sorry, I somehow thought you would compare only two images [based on your formula (A2 - A1/(A2 + A1)

Then multi-temporal filtering could be worth a try! The time-series is long enough to distinguish between stable change and random variations.

I read your post,here,I had the same matters wtih you,which I need to do an analysis for the time-series with S1 SLC datas ,I try many times ,but failed.could you help me ? the steps please .
thanks in advance.