S1 & S2 Land use/land cover classification using deep learning

Question

Hello, I'm working on deep learning land use and land cover classification with Sentinel 1 and Sentinel 2 data.
I used the composite bands to stack my data, and I used the standard deep learning workflow.
I'm doing a binary classification right now ( Forest - a class value of 0, Non-forest - a class value of 1).

When I used my deep learning model to classify the pixels, the result was a black image.

Anyone have any suggestions for how I could solve this? I will surely appreciate.

RDAltarez · Answer

Thank you @andrei_ste  for your response. I followed the steps provided in this link https://community.esri.com/t5/arcgis-pro-questions/help-with-deep-learning-and-pixel-classification/td-p/1081251/page/2. I was able to generate my classes, at last! I used a TIFF file of my Sentinel 2 (RGB) as my input. Yahoo!

andrei_ste · Answer

I read your first post and you said that you used composite bands to stack your data. I think you should use instead the metadata of your satellite image. Also are you using the pretrained model from Esri or are you using a trained model created by you or someone else? You could use the pretrained model from Esri as a test to check if everything is set up ok: https://www.arcgis.com/home/item.html?id=afd124844ba84da69c2c533d4af10a58

There are detailed instructions on how to use the model in a pdf that you can find in the description. If the steps provided work than it must be something related to your trained model or input parameters.

RDAltarez · Answer

Hi @andrei_ste  thank you for your reply. I did follow this link before doing my deep learning I believe, I have installed 347 packages. Please see below. 
I also found other way to get away with the "Unbound error...". I used Jupyter notebook, and used this script (see attached).

Unfortunately, when I used the created model, the resulting image is clear. There's no classified image. See image below.

script.zip