I have a small (processed SAR image, RGB) area of 40 * 40 km approx. where I want to extract water bodies (it's an estuary during flooding basically), i.e. apply pixel classification (I assume object class. isn't for this application).
1. Export training data - I select 20 samples for water and 20 for wetlands, randomly over area, no issues with export (mask used to reduce the area), selected classified tiles
2. Train data for deep learning - using the aforementioned exported data and U Net model (tried several cases up to 60 epochs with different batch sizes, my GPU has 8 GB and that shouldn't be an issue).. I try to train the model but results are showing 0 good predictions and 0 pixels classified in my selected classes.
3. Classified pixels using deep learning - since previous steps failed, makes no sense to start this yet...
I am relatively new in deep learning and wondering does it even make sense to apply it while studying only 1 single satellite image ?
I can do this task very easily with the same samples with traditional image classification, but still don't get why deep learning isn't working.
Attached:
-Image with samples
-Train results
Cheers