Rndom forest classification steps


#21

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?


#22

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
#23

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


#24

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


#25

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


#26

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.


#27

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:
http://wgrass.media.osaka-cu.ac.jp/gisideas10/papers/04aa1f4a8beb619e7fe711c29b7b.pdf

Please also use the search function for common questions: