Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data

TL;DR

This paper explores the application of deep learning and traditional change detection methods to hyperspectral satellite data from PRISMA, highlighting the potential and challenges of hyperspectral change detection in environmental and urban monitoring.

Contribution

It introduces a pipeline combining coregistration, spectral-based, and deep learning change detection methods specifically for hyperspectral satellite data from PRISMA.

Findings

Vegetation and urban changes are effectively detected.

Deep learning methods are more robust to noise than statistical methods.

Spectral information helps identify subtle environmental changes.

Abstract

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.