How to handle extremely 'long' images? After transforming timeseries into an image format, I get a width-height ratio of ~135. Typical image CNN applications involve either square or reasonably-rectangular proportions - whereas mine look nearly like lines:



Example dimensions: (16000, 120, 16) = (width, height, channels).

Are 2D CNNs expected to work well with such aspect ratios? What hyperparameters are appropriate - namely, in Keras/TF terms, strides, kernel_size (is 'unequal' preferred, e.g. strides=(16, 1))? Relevant publications would help.



Clarification: width == timesteps. The images are obtained via a transform of the timeseries, e.g. Short-time Fourier Transform. channels are the original channels. height is the result of the transform, e.g. frequency information. The task is binary classification of EEG data (w/ sigmoid output).

Relevant thread
I had recently used a slightly unorthodox method to process such images, which involved using RNNs.

Assume the image dimensions to be (16000, 120, 16) = (width, height, channels), as in the question.

Apply a 2D convolution (or multiple such convolutions) of shape(1, k, c),
such that the output of the convolutions becomes (16000, 1, c). So if you only use a single convolutional layer, k=120.

Then, squeeze the extra dimension, to get the shape (16000, c). 

The problem has now been transformed back into a sequence problem! You can use RNN variants for further processing.