Investigating the Potential of Deep Learning Approaches in the Reconstruction of VNIR-SWIR Hyperspectral Data From Multispectral Imagery

Abstract

Hyperspectral (HS) satellite data are of considerable importance for applications such as environmental monitoring and precision agriculture, given the richness of the spectral information they contain. However, HS data typically exhibit limited spatial resolution and are less readily available than multispectral (MS) data. This study, which aims to simulate data with high spectral and spatial resolution, explores the use of attention-based spectral reconstruction (SR) techniques, specifically MST++, MIRNet, AWAN, and Restormer, to derive HS data in the visible near infrared (VNIR) and short-wave infrared (SWIR) from MS imagery. High-resolution MS and HS image pairs are generated from AVIRIS-NG aerial data and employed for training procedures, thereby enabling the reconstruction of HS data that closely resembles the original measurements. The results indicate that SR techniques can considerably enhance the utility of existing MS datasets for HS-dependent applications. Such techniques can effectively be employed to synthesize high-resolution HS data from MS inputs, thereby facilitating the potential for developing a comprehensive end-to-end sensor simulator. This is particularly advantageous in the context of simulating data from a mission that has not yet become operational, as exemplified by the PRISMA-2G data, which could be simulated, for example, from Sentinel-2 data.