Unmixing based PAN guided fusion network for hyperspectral imagery

TL;DR

This paper introduces a lightweight unmixing-based fusion network that effectively combines low-resolution hyperspectral images and panchromatic images to produce high-resolution hyperspectral images, especially at large fusion ratios.

Contribution

The proposed Pgnet addresses ill-posedness in hyperspectral-pan fusion and operates under a low-dimensional abundance subspace with a large fusion ratio of 16, introducing an interpretable PAN detail inject network.

Findings

Outperforms several state-of-the-art methods qualitatively and quantitatively.

Effectively handles large fusion ratios like 16.

Demonstrates superior fusion performance on simulated and real datasets.

Abstract

The hyperspectral image (HSI) has been widely used in many applications due to its fruitful spectral information. However, the limitation of imaging sensors has reduced its spatial resolution that causes detail loss. One solution is to fuse the low spatial resolution hyperspectral image (LR-HSI) and the panchromatic image (PAN) with inverse features to get the high-resolution hyperspectral image (HR-HSI). Most of the existing fusion methods just focus on small fusion ratios like 4 or 6, which might be impractical for some large ratios' HSI and PAN image pairs. Moreover, the ill-posedness of restoring detail information in HSI with hundreds of bands from PAN image with only one band has not been solved effectively, especially under large fusion ratios. Therefore, a lightweight unmixing-based pan-guided fusion network (Pgnet) is proposed to mitigate this ill-posedness and improve the fusion performance significantly. Note that the fusion process of the proposed network is under the projected low-dimensional abundance subspace with an extremely large fusion ratio of 16. Furthermore, based on the linear and nonlinear relationships between the PAN intensity and abundance, an interpretable PAN detail inject network (PDIN) is designed to inject the PAN details into the abundance feature efficiently. Comprehensive experiments on simulated and real datasets demonstrate the superiority and generality of our method over several state-of-the-art (SOTA) methods qualitatively and quantitatively (The codes in pytorch and paddle versions and dataset could be available at https://github.com/rs-lsl/Pgnet). (This is a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block.)