Hello all,
I have been attempting to classify 0.5 m imagery for my research. Unfortunately, when I go to run the "classify pixels using deep learning tool", I get an output with no errors, but the raster is seemingly blank.
I have collected extensive training points on all of the various land cover/habitat types of interest, exported the training data for deep learning, and trained the deep learning model. The following are my specifications for the "train deep learning model tool"...
I have also run this with the Deeplab model (which is what I initially planned on using) with the same outcome. I subsequently run the "classify pixels using deep learning model" tool, using the .dlpk file that I get as output from the previous tool. The following is the raster info from the image I want to classify...
It appears as though the output raster that is supposed to be my classified image has pixels that have values of either 0 or 255, but when displayed on my map, it seems completely blank.
Other questions I have seen about this say to make sure your image is 8 bit unsigned, which mine is. I must be missing something here, this has been a very "learn as I go" experience, so any help would be much appreciated.
P.S. I'm working with ArcGIS Pro 3.0.3 and have downloaded the required deep learning libraries associated with the tools that I am using.
Accepted Solutions
Hi @CodyGoodson
I wonder if the raster is created right but not its colormap in the attribute table of the raster. Please right click the out raster in the Content pane and open its attribute table. Please see if the values in the table match with the emd file of the model. I am pasting an example from one of my models.
If the attribute table is not correct, you may consider this to workaround: make a copy of your raster, and delete the attribute table by using the tool, and see if the display is correct. This is just a workaround if the colormap is not created correctly but we can look into why it happened.
Below image shows an example of a model emd file.
Output raster and its attribute table for the above model
About your second question, it depends on what raster you used to train the model e.g. was it a 3-band raster or 8-band. If the model is trained with 3-band and input for inferencing is an 8-band raster, the tool will return an error related to not finding bands. About pixel depth, I am assuming the training data's values range from 0-255 and so do your input rasters.
Hi @CodyGoodson
I wonder if the raster is created right but not its colormap in the attribute table of the raster. Please right click the out raster in the Content pane and open its attribute table. Please see if the values in the table match with the emd file of the model. I am pasting an example from one of my models.
If the attribute table is not correct, you may consider this to workaround: make a copy of your raster, and delete the attribute table by using the tool, and see if the display is correct. This is just a workaround if the colormap is not created correctly but we can look into why it happened.
Below image shows an example of a model emd file.
Output raster and its attribute table for the above model
About your second question, it depends on what raster you used to train the model e.g. was it a 3-band raster or 8-band. If the model is trained with 3-band and input for inferencing is an 8-band raster, the tool will return an error related to not finding bands. About pixel depth, I am assuming the training data's values range from 0-255 and so do your input rasters.
