  We explore feature space geometries induced by the 3-D Fourier scattering
transform and deep neural network with extended attribute profiles on four
standard hyperspectral images. We examine the distances and angles of class
means, the variability of classes, and their low-dimensional structures. These
statistics are compared to that of raw features, and our results provide
insight into the vastly different properties of these two methods. We also
explore a connection with the newly observed deep learning phenomenon of neural
collapse.
