Hello everybody.
I am a PhD student trying to apply ESRI's pretrained deep learning models (SAM and/or Agricultural Field Delineation) to declassified satellite imagery, specifically Corona or Hexagon low-resolution mapping camera images.
In a different forum I was told that as these are single-band images, I would need to add texture to them (e.g. integrating Landsat bands to them) to make them multiband images for this type of analysis. Which I did. I was also advised that utilising a DEM that corresponds to my study location and approximate time period (of the imagery) would improve my results. So, I found the appropriate DEM and clipped it to my study area. A different expert said that the deep learning is more effective when DEM-derived products are also included in this analysis, specifically slope and curvature.
I now have 4 different raster images: the textured declassified image, an SRTM DEM, slope and curvature. I uploaded them individually to ArcGIS Online to experiement with the aforementioned deep learning packages. I tried both packages on just the textured image and on a composite band image. And received error messages every single time. I'm not an experienced GIS-user so it is possible that I have made some dumb mistakes in preparing my images for this type of analysis. But at this point, I'm not sure where to even start troubleshooting.
Has anybody here ever successfully used one of these pre-trained models on this type of declassified imagery? Or heard of anybody who has been able to do so? If so, I would appreciate any advice, contacts, workflows, recommended reading, etc. in order to move forward. This data preparation is only a small (although potentially invaluable) part of my total research but I need to be extremely careful to not get bogged down in lengthy, time-consuming tasks (e.g., accumulating individual training samples)...or anything too complicated for somebody that doesn't know coding or in-depth GIS programming.
Thank you, in advance, for any help!
