Hello there,
I want to run image classification on Sentinel 2 image. However, training samples are required to run a supervised classification. How do I create training samples on RGB view image in SNAP?
You find the basic steps here: Supervised and unsupervised classification, Sentinel 2
Same topic, also good explanation: Rndom forest classification steps
Some notes on creating the vectors in the correct container: Drawing vectors
also here: Supervised and unsupervised classification, Sentinel 2
I see… I’ve been using the wrong tools… Thanks !
In supervised image classification, you need to train the classifier to assign pixels or objects to a given class using training samples. The class categories are determined by your classification schema, and the training samples can be generated using the Training Samples Manager pane. Tools in the Training Samples Manager pane allow you to create training samples for each class category in your schema and provide information about the number and size of samples to help you improve the accuracy of your classification model.
The Training Samples Manager is found in the Classification Tools drop-down menu in the Image Classification group on the Imagery tab. Select the raster dataset you want to classify in the Contents pane to display the Imagery tab, and be sure you are working in a 2D map.Classification Tools will be disabled if the active map is a 3D scene, or if the highlighted image is not a multiband image.