I have just discovered the TWS plugin in FIJI. I plan on using it to distinguish between bare ground (rock and soil), dead/unhealthy vegetation, and living/healthy vegetation in RGB digital photography images of country ground. I have about a thousand 4.6million pixel images to classify. The digital camera used isn’t the same for all images. Ultimately, I need the proportion of each class for each image.
The fast random forest seemed to perform a good segmentation for one of my images which has no shadow present.
The problem is that some images have dark shadows in them so I’m wondering what features will be necessary to perform accurate segmentation for these diverse images. I’ll attach some of them as an example. Some of my images are very similar because they are of the same location on a different date. The images uploaded show the diversity.
I also have a txt file of training data with rows: R,G,B, label of 200 samples for every image if that can somehow be imported into Weka to train a classifier (this would be ideal). Otherwise I can manually create training data myself. Also, do you have any specific model recommendations? I should I experiment.
Any advice would be greatly appreciated. Cheers, Sam.