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.

翻译：变化检测（CD）方法已应用于光学数据数十年，但利用高光谱分辨率数据进行变化检测的研究仍鲜有探索。CD广泛应用于环境监测、灾害管理等诸多领域。依托于PRISMA（PRecursore IperSpettrale della Missione operativA）卫星，星载高光谱变化检测现已可行。本研究将标准变化检测方法与深度学习（DL）方法应用于从自然区域到城市区域的不同目标。我们提出了一套流程：首先进行图像配准，随后采用全光谱算法进行变化检测，并应用为光学数据开发的深度学习网络。研究发现，植被与建筑环境的变化能被有效捕捉。光谱信息对于识别细微变化具有重要价值，且相比统计方法，深度学习方法受噪声影响更小，但大气效应以及可靠真值数据的缺失仍是高光谱变化检测面临的主要挑战。