We have three input image layers for the same geographic region and would like to classify the area using the information from all the three inputs. We created a TIFF file with three bands, each is 8-bit. This was treated as a RGB image in 2D WEKA segmentation and we noticed that, if we just select the “Mean” feature, the final feature list for each training pixel includes: original, H, S, B, Mean_1, Mean_2, …, where original=(R+G+B)/3, and Mean_1, Mean_2, … all based on (R+G+B)/3. We were hoping to have, instead, are, at least for the “Mean” features, Mean_R_1, Mean_G_1, Mean_B_1, Mean_R_2, Mean_G_2, …Is there a way to achieve this?
We did try to create a 3-slice TIFF file using ImageJ’s stacking tool, each slice is 32-bit. For a given training location/pixel, we had to add it to a class independently for each slice and it resulted in three samples/rows in the feature list table. Again, we are hoping to have the features to be derived for each input layers simultaneously for the same location/pixel. Ideally, clicking “Add to Class” once, resulting in one sample/row in the feature table. All suggestions are welcome.
Thanks in advance!