Rndom forest classification steps


glad to see this worked.
Did the reprojection change your values in any way? If I undestand you correctly you selected WGS84 in the terrain correction and then reprojected also to WGS84?


I reproject the data into Geographic Lat/Lon. After that, the values do not change.
I try some times with some different data and I find out that in the classification step, it works with the Lat/Lon project.

Supervised and unsupervised classification, Sentinel 2
"bound must be positive" error in the Random Forest Classifier
Supervised Classification with Sentinel-2
Profile of a colour image
Image Fusion Using Sentinel 1 and Sentinel 2

good to know, although it is ab bit strange that this step is required.


hi , i need help
you have created polygon on sigma0_hh_db,s o how you had applied on _glcm product ,as both are different
or i have to stack sigma0_vv_db band to _glcm product.
where to give polygon on sigma0_vv_db or any one of the glcm product


create the GLCM layer and stack it with your oiriginal Sigma0 file. The final product will contain both the intensity and the textures.


after following your step ,i got this classification

can help in identifying the black area
as that is a river area, but i made two classes blue showing water area , then why black?
and what does confidence image tells.


areas which are not classified do not fulfill the confidence criterion of Random Forest, that means they cannot be assigned to one of the classes.
You should read a bit about the classifier before applying it:

Please also use the search function for common questions: