I'm new to this field of study.
my teacher ask me to do a hybrid MLC and ISODATA classification for my bachelor thesis. so I got one journal that combine both method, but I think it too complex for bachelor degree because it's also using a decision tree, which I don't understand at all.
so I was thinking, what if I made one for myself, a simple one but useful, accurate, and easy to apply. here how it goes:
I classify image with ISODATA, and then get the result, if accuracy assessment value is low, we redo the classification, if high, we use that result as training area for MLC. Then after we classify the image with MLC, we compare the result also from MLC but with training area from ground truth.
The conclusion will be, if both result are similiar, that mean we don't have to do ground truth anymore to get training area for MLC, we can just use ISODATA to get the training area and then use the result as training area for MLC.
I'm planning on doing all of this with ERDAS IMAGINE 2014
Can you give me your opinion for this method?