I've created spectrogramms of different classes of low-hertz signals. They all have a plain blue foreground with hardly other coloured pixels, even for me its not easy to distinguish the classes by human sight. Now I'd like to train a CNN to do binary classification on these spectrogramm images. No matter how I build the network with no matter what configurations and parameters - it doesn't learn, loss doesn't decrease.
I figured out the reason: the convolutional filters of CNNs are good at distinguishing forms and shapes, but not really plain colours.
How can CNNs learn colours? Am I missing something? Perhaps there are more suitable models than CNNs?
Here is an example of the two classes that hardly differ, only in the colour hue:
2 more images:
EDIT1: The two classes have images that not all look the same with the same prevailing colour; some have yellow/greenish stripes in them, some have lighter or darker colour etc. So the classes do not only consist of the feature colour. My goal is to classify these images using CNNs, but somehow CNNs fail at learning from images, that almost only consist of colour, whereas CNNs learn well if there are sharp edges, boundaries, object-like elements in the image.