@ABraun it doesn’t work, so what must i do now? its stuck
@marpet hmmm okay, thanks for your answer marco, Do you have another solution?
anyway, is it possible if i want to build DSM using S1 SLC product?
@ABraun it doesn’t work, so what must i do now? its stuck
Did you apply sen2cor on your Sentinel-2 product? Maybe this caused the ‘.raw’ file.
technically, no. Interferometry always requires two products. But also with an image pair you won’t get a proper DSM out of S1 because c-band is too sensitive towards changes over vegetation, for example. It worked in some cases, as you can see here:
no, i did not apply it, so… is it possible to fuse dem from S1 with S2 data?
It seems the bands are renamed but the valid expressions are of the bands are not.
Right-click on the band, choose Properties and remove the valid expression.
I want to coregister Sentinel-1A imagery and Sentinel-2A imagery. i have taken subsets of both the images.
On Sentinel-1A, I have performed calibrtaion, multilooking, speckle correction and terrain correction.
When I am trying to coregister both the images, following error is being shown:
Error [NodeId: Create Stack] Orbit offset method is not support for this product. Doppler Centroid coefficients not found.
This is the initial offset selection in the CreateStack. There isn’t orbit information in the Sentinel-2 product so you’ll need to use Geolocation.
Follow the advice above by @mengdahl and terrain correct the SAR and use the same map projection with the optical then just use CreateStack with Geolocation for the initial offset and bilinear resampling.
After the collocation process what should be done to fuse the images.
There are several approaches - depends on your application.
If you want to classifiy the image based on both S1 and S2, you can directly digitize training areas on the stack. The Random Forest classifier, for example, then uses information of both sensors.
If you really want to merge the images you can apply a PCA which generates new layers based on information of both sensors.
There are also various other fusion techniques, feel free to research and compare them.
Actually i will be fuse S1 and S2 and RF classification will be used in SNAP. I am little bit confuse that after collocation process the PCA should be used or something else? Please clarify me plzzzz.
Come on… How can I know what you are aiming at? We are just trying to help, but there are so many fusion techniques, there is no standard procedure and there are many things which have to be considered. So how can we advise you if you obviously don’t know what you want yourself.
If you want sound advice, give a bit more information by yourself. What is your data, what do you want to do with it, which method did you chose…
Just heading from one step to the next makes it so difficult for us to understand what is going on at your side.
I have sentinel 1 A and 2B data. I want to do LU/LC classification in SNAP using RF classification. So i want to use fusion technique foe more accurate classification. If more clarification is required please let me know.
In this case you can simply train your samples on the stack which contains both S1 and S2 data and then apply the Random Forest classifier.
You will find descriptions here:
Once S1 images are terrain corrected, re-projected in UTM (as S2) and stacked together with S2, I guess the geo-location accuracy of overlapping S1/S2 pixels in the stack is defined by the orthorectification algorithm.
Which level/order of accuracy can we expect in terms of pixel alignment?
From my understanding, it should be sub-pixel order. Any comments on that?
hello ABraun, I was wondering if it is important to prepeocess the sentinel-2 data because I tried classifying it and the result wasn’t good…in fact it didn’t work.
I don’t know whether it is because I didn’t preprocess it or maybe I might have left something out but I followed this your discussion with dini_ramanda and did exactly as he/she did.
hello dini_ramanda. how did you go about your classification of the sentinel-2 dataset. I am getting something strange but the sentinel-1 works perfect
Radiometric correction only slightly changes the pixel values, it is not mandatory for classification (unless you want to compare scenes of different dates).
But there were cases where classification only worked after reprojection.
hello, i am doing S1 and S2 fusion, i did calibration, speckle filtering, terrain correction of s1 data from Snap as S1 preprocessing. i did nothing to S2 data,
then for fusion i need to do co-registration of both data on same reference coordinate scale. I need step how to do coregistration in detail with coordinate reference help.
and what will be future steps to do fusion,. i need help,
if you’ve terrain corrected the S1 products, just use Collocate or CreateStack on the S1 and S2 products.
hello , thanks for your suggestion.
Now i did collocate from SNAP raster geometric.
is collocate, create stack and coregistration is same thing?
now , what next i do for fusion of SAR and optical(PCA, IHS, Brovey etc), and is it possible from SNAP or i have to export into ERDAS.
kindly guide me , i am new remote sensing
If you are new to the field, take your time to study and compare some opinions and approaches. There is no standard way of fusion, so it also depends on what you want to do with the data after the fusion. I listed some references to fusion approaches here: Fusion of S-1A and S-2A data
It might be suitable to convert the SAR data to db (also right-click on the SAR bands for this) because this creates a more suitable distribution of backscatter values. Explanations are given here: dB or DN for image processing? and here Classification Sentinel-1 problems with MaxVer
Once they are in your stack you can create an RGB image by right-clicking on the product and selct “Open RGB image window”. This lets you allow to place colors on different bands and shows you their different information content.
Some fusion methods also can be done in the band maths tool (right click > band maths)
PCA is available under Raster > Image Analysis > Principal Component Analysis.
Maybe also an unsupervised clustering is an option to you (Raster > Classification > Unsupervised Classification)