Synthetic Abundance Maps for Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images

TL;DR

This paper introduces an unsupervised hyperspectral image super-resolution method that uses synthetic abundance maps generated from a dead leaves model, eliminating the need for high-resolution ground truth data.

Contribution

The authors propose a novel unsupervised framework leveraging synthetic abundance data for hyperspectral super-resolution, avoiding reliance on ground-truth references.

Findings

Effective across multiple datasets and scaling factors.

Synthetic abundance data enhances training without ground truth.

The method outperforms existing supervised approaches in experiments.

Abstract

Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, most existing methods are supervised and require ground truth data for training-data that is often unavailable in practice. To overcome this limitation, we propose a novel unsupervised training framework for HS-SISR, based on synthetic abundance data, where no high-resolution ground-truth reference is required for training. The approach begins by unmixing the hyperspectral image into endmembers and abundances. A neural network is then trained to perform abundance super-resolution using synthetic abundances only. These synthetic abundance maps are generated from a dead leaves model whose characteristics are inherited from the low-resolution image to be super-resolved and from the known point spread function (PSF) of the hyperspectral sensor. This trained network is subsequently used to enhance the spatial resolution of the original image's abundances, and the final super-resolution hyperspectral image is reconstructed by combining them with the endmembers. Experimental results demonstrate both the training value of the synthetic data and the effectiveness of the proposed method across 3 datasets, 3 scaling factors, and several evaluation metrics. The code is available at https://github.com/xinxinxu99/SISR-DL.git