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.

利用PRISMA的高光谱数据进行变化检测，在不同的目标（从自然到城市区域）中应用了标准和深度学习方法，发现植被和建筑环境的变化能够被很好地捕捉到，但大气效应和可靠的地面真实性缺失是高光谱变化检测的主要挑战。

基于深度学习的PRISMA卫星高光谱数据变化检测