Sentinel 1 land cover classification

[ABraun]

this is a short description on image classification in SNAP

Supervised and unsupervised classification, Sentinel 2

if the unsupervised classification took too long you can subset your image and test it (a whole S2 scene could take really long for a cluster or EM analysis) reduce the number of clusters Choose another classifier (EM and K-means take different time) For a supervised classification you have to define geometries including your training areas. [image] Make one per class you want to detect. [image] They are stored in the vector Data folder (don’t mind about pins and gcps) [image] You can t…

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

EnMAP

Random Forest NAN at Confidence values below 0.5

@dreckbolle: This solution is not SNAP related, but I achieved great results with the ENMaP-Toolbox. You need the IDL virtual machine (installation of IDL, needs no license) for it and can load the DIMAP rasters directly into the program. You can also import Shapefiles as training vectors so no conversion of your data is needed. Also its computing time is impressive (raster with 5000*8000 and 50 bands takes about 3 minutes). Is quite simple to use and allows for changes in the RF parametrizat…