Sentinel 1 land cover classification

I’m looking to do a landcover classification of Sentinel 1 sar data in either SNAP or QGIS. Does anyone know how to go about doing this?

Please take a look at this

Thank you so much! I’m afraid I’m not very good at programming. I was hoping there is a tool I could run my image through to generate a classification. I have tried the Texture Analysis Using the Gray-Level Co-Occurrence Matrix (GLCM) in SNAP and have tried the Semi auto automatic classification plug-in in QGIS but my computer keeps crashing, so I’m unsure if these methods work or not. Do you know should these methods work?

If you read the abstract of the article I sent it to you within my previous post carefully, you could extract the procedure or method according to what you want to . implement, and then according to your method you could find out or answer this question yourself, Could I use snap to reach my goal?

Thank you :slight_smile:

this is a short description on image classification in SNAP

If you don’t want to use SNAP you can try the EnMAP toolbox

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the methods (collection of samples and training/application of classifiers) work with SAR data as well. Especially the random forest classifier takes input of different units so SAR intensities and textures can be applied.

I would however suggest to convert to Sigma0 db so the data range is roughly between -40 and +5.

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Hi there

just making sure that I’ve understood this procedure correctly.

First I go wit h this procedure
Second, I convert into Sigma0 db --> How, what steps in SNAP?
Third I go with your proposed "Supervised and unsupervised classification, Sentinel 2

Done! correct?

Kind regards

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yes. Multi-looking is not necessarily required but improves your data regarding the presence of speckle.

You can convert your data by right-clicking on the bands > “Convert to db”
This generates a virtual raster. Make it physical by right-clicking “convert band” and save your product.

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Thank you ABraun for that fast answer.
I’m comming from the interferometry field…and was kind of lost in this topic.
Thank you and have a nice weekend.

Good luck. You can share your results in here with screenshots and we can maybe help again if you are stuck somewhere.

I am having my training data sets as point file. Is it possible to classify S1 data in SNAP using points?

I don’t think so. In any case for SAR data using a single pixel would be bad.

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If I have the polygons, is it possible to do supervised classification in S1 images with SNAP?

yes. Open your SAR image, menuy > import > other > shapefile
Then use the polygons as training vectors in the classification modules by selecting the column that has the class information.

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How do you execute a Support Vector Machines classification to map rice areas in Sentinel-1 toolbox? thanks before

SVM is currently not implemented in SNAP

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Thanks for the reply ABraun. Do you have any suggestions on what classification method i can use to map rice areas that can be achieved with SNAP Sentinel-1 toolbox? My data is IW GRDH by the way

the random forest classifier is quite good if you have data of different scales. But it needs more than just 2 rasters.
For the case you only have VV and VH you can use the KD Tree KNN classifier.

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Okay Abraun how do you execute a KD Tree KNN classifier on SNAP? I’ve tried looking at and the ESA training courses but couldn’t find what was needed. Sorry for the many questions.