i use SLC product,what’re the steps?
please tell me what you plan to do witht the data. The steps depend on your aims.
As a matter of fact I wanna export the growth rate of population by buildings
If your approach does not include radar interferometry or polarimetry, I suggest to use GRD products then. SLC data contains phase information at slant resolution, but after pre-processing, you end up with a ground resolution of at least 10 by 10 meters which is identical to what you can download as GRD product. This would save you a lot of downloading and processing time.
In this post, standard pre-processing procedures are suggested: Radiometric & Geometric Correction Workflow
As you mentioned radar interferometry is my requirement,
If my researching area be splited it would-be fine
Split has to be performed on every SLC product because of the file structure. First you select the smallest number of bursts required to cover your area (see here). Once you have debursted the data, you can make a smaller subset.
By the bursts I’ve splited my Area as small as I could,
But by debursting I have no idea
You simply execute it after splitting to merge the bursts. After that you can create a subset.
Maybe the InSAR tutorials help you, although they are not aiming at urban areas in special
I’ve done this process(Sentinel-1 TOPS interferometry) not problem about it,
My problem is cuting my researching area
And the processes that should I go for my images
Echoes_In_Space_Urban_Footprint_Tutorial.pdf (2.3 MB)
The two images in file are for example but I should finish the all steps for my image in pairwise
You should have mentioned earlier that you are referring to avoid confusion.
In this tutorial the TOPS Split operator is referred to as “Data subset”, but it is actually just a required step to work with the data structure of Sentinel-1 IW SLC products.
So at which point do you have problems?
Step 13 can only be computed from two images of the same track.
Yes I know about step13
In pdf file you see that the city area is full screen it doesn’t have any another feature like mountains and lakes,…
For my project like that I just wanted to have my researching area lack of another feature ,but the method that you said me(spatial subset from view)
I split me researching area,
yes, you can reduce the extent of the image after deburst (step 15), but there will always be other things in the image, even in the PDF there are water bodies and mountainous areas. This is why they suggest the color composite, so the urban area is enhanced.
In your example, you have created the subset on the Split product but Deburst should be applied before that actually.
My meant was about extensive feature,
Ok I would split my product(by subset) after deburst analysis
And thank you for your patience ,I appreciate
the names differ according to your data, of course. I can’t tell where the mst in your workflow went, but you can still calculate a difference.
Is there a reason why you use images of 2017 and 2019?
For good coherence you should rather combine data with 12 or 24 days inteval, as suggested in the tutorial. I also see an offset between image 1 and 2 in your image, the mean seems blurred.
The reason:I wanna compare the growth rate in four years
Once:2019-2016
Once:2019-2017
Once:2019-2018
The approach from the PDF is not designed for multi-temporal analysis.
In this case, you need short baseline image pair for each yeat first to compute the annual urban area and then compare these in a second step.
Yes professor,
Hi professor,if you remind my project (if you don’t our conversations is in hand) I’ve finished the steps four in three times :
First:2017-2018
Second:2017-2019
Third:2017-2020
Now I’ve three as image consists of buildings
I wanna calculate the Changs with “and” , “or”
And find these areas:
1-the area that aren’t residential(not now not before)
2-the area that weren’t residential but now are residential
3-the area that were residential but now have been destroyed
4-the area have been residential and now are too,
Foremost second option is important for me ,