Multi-Band Image Fusion Based on Spectral Unmixing

TL;DR

This paper introduces a novel multi-band image fusion method that leverages spectral unmixing and an alternating optimization approach to enhance the quality of fused images, outperforming existing algorithms in accuracy.

Contribution

It proposes an unsupervised spectral unmixing-based fusion algorithm combining physical constraints and an efficient optimization strategy for improved image quality.

Findings

Improved abundance and endmember estimation over state-of-the-art methods

Effective fusion of high-spatial low-spectral and low-spatial high-spectral images

Validated on synthetic and semi-real datasets with superior results

Abstract

This paper presents a multi-band image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the likelihoods of the observations. The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem. The joint fusion and unmixing problem is then formulated as maximizing the joint posterior distribution with respect to the endmember signatures and abundance maps, This optimization problem is attacked with an alternating optimization strategy. The two resulting sub-problems are convex and are solved efficiently using the alternating direction method of multipliers. Experiments are conducted for both synthetic and semi-real data. Simulation results show that the proposed unmixing based fusion scheme improves both the abundance and endmember estimation comparing with the state-of-the-art joint fusion and unmixing algorithms.