I am trying to classify 9 different classes of Land use deploying Resnet Architecture. Sentinel images come with 13 bands and in RS we normally combine different bands for different classes for example for agriculture color bands (11,8,3) are composites for agriculture, and so forth with RGB, True Color, NDVI.
I have tried combining different bands and so far accuracy is 0.83 with RGB. I tried SWIR and True Color combinations and both have lower scores.
In addition, I tried to have different and appropriate bands for each class, for example, Residential(Bands 4,3,2), River(8,11,4), and Crops(11,8,2). Well, this has improved the accuracy of the model up to 0.93.
My question is twofold:
Is this really an acceptable approach in Deep Learning practice? All the literature I have seen so far are using the same bands for each class.
If this approach is ok, then can I use different band combinations for the same class as data augmentation? this will increase data size and hence improve the accuracy.