  Change detection (CD) methods have been applied to optical data for decades,
while the use of hyperspectral data with a fine spectral resolution has been
rarely explored. CD is applied in several sectors, such as environmental
monitoring and disaster management. Thanks to the PRecursore IperSpettrale
della Missione operativA (PRISMA), hyperspectral-from-space CD is now possible.
In this work, we apply standard and deep-learning (DL) CD methods to different
targets, from natural to urban areas. We propose a pipeline starting from
coregistration, followed by CD with a full-spectrum algorithm and by a DL
network developed for optical data. We find that changes in vegetation and
built environments are well captured. The spectral information is valuable to
identify subtle changes and the DL methods are less affected by noise compared
to the statistical method, but atmospheric effects and the lack of reliable
ground truth represent a major challenge to hyperspectral CD.
