  Hyperspectral imaging, a rapidly evolving field, has witnessed the ascendancy
of deep learning techniques, supplanting classical feature extraction and
classification methods in various applications. However, many researchers
employ arbitrary architectures for hyperspectral image processing, often
without rigorous analysis of the interplay between spectral and spatial
information. This oversight neglects the implications of combining these two
modalities on model performance.
  In this paper, we evaluate the performance of diverse deep learning
architectures for hyperspectral image segmentation. Our analysis disentangles
the impact of different architectures, spanning various spectral and spatial
granularities. Specifically, we investigate the effects of spectral resolution
(capturing spectral information) and spatial texture (conveying spatial
details) on segmentation outcomes. Additionally, we explore the transferability
of knowledge from large pre-trained image foundation models, originally
designed for RGB images, to the hyperspectral domain.
  Results show that incorporating spatial information alongside spectral data
leads to improved segmentation results, and that it is essential to further
work on novel architectures comprising spectral and spatial information and on
the adaption of RGB foundation models into the hyperspectral domain.
  Furthermore, we contribute to the field by cleaning and publicly releasing
the Tecnalia WEEE Hyperspectral dataset. This dataset contains different
non-ferrous fractions of Waste Electrical and Electronic Equipment (WEEE),
including Copper, Brass, Aluminum, Stainless Steel, and White Copper, spanning
the range of 400 to 1000 nm.
  We expect these conclusions can guide novel researchers in the field of
hyperspectral imaging.
