Hi - I attempted to run an object classification on 3" resolution, 4 band imagery of about a mile square area of a river channel. Looking to identify Arundo (an invasive bamboo species), Water, Bare Earth, Palm Trees, Other Trees, Grass/Vines (low vegetation). I got some very un-expected results, the output was a polygon layer arranged in a grid pattern of equal size rectangles (see attached screen shot of output). My process was:
0) Image Prep
Mosaiced geo tifs together using 'Mosaic to New Raster'
'Extract by Mask' to the study area
1) Label Objects for Deep Learning Tool (approx 300 samples of each class)
2) Export Training Data for Deep Learning Tool
Meta Data type - Labeled Tiles
3) Train Deep Learning Model Tool - use all defaults for parameters
Max epochs - 20 (default)
Model Type - Feature Classifier (default)
4) Classify Objects using deep learning Tool
Any ideas why I'm getting this grid pattern output? I expected detailed boundaries with curves around different classes. It also took several days to run the classification (way too long I think) -- it classified areas that I had masked off with Extract By Mask, which maybe added confusion. I'm primarily interested in the boundaries of the Arundo. I tried an Object extraction, but that didn't provide boundaries of the Arundo either, just squares. Also attached is my model metrics, which I thought looked pretty good
Any thoughts would be much appreciated
Thanks
Pete
