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?
this is a short description on image classification in SNAP
If you don’t want to use SNAP you can try the EnMAP toolbox
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.
just making sure that I’ve understood this procedure correctly.
First I go wit h this procedure http://sentinel1.s3.amazonaws.com/docs/S1TBX%20SAR%20Basics%20Tutorial.pdf
Second, I convert into Sigma0 db --> How, what steps in SNAP?
Third I go with your proposed "Supervised and unsupervised classification, Sentinel 2
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.
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.
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.
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
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.
Okay Abraun how do you execute a KD Tree KNN classifier on SNAP? I’ve tried looking at http://step.esa.int/main/doc/tutorials/ and the ESA training courses but couldn’t find what was needed. Sorry for the many questions.