Exploring the high dimensional geometry of HSI features
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.PDF Abstract
Results from the Paper
Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.
No methods listed for this paper. Add relevant methods here