# Multi-Band Image Fusion Based on Spectral Unmixing

arXiv: https://arxiv.org/abs/1603.08720 · HTML: https://mlsanity.com/paper/1603.08720/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2016.

## Built on by (most cited first, 11 of 11)

- [Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2111.13923/) (2021)
- [A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring](https://mlsanity.com/paper/2409.18731/) (2024)
- [Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion](https://mlsanity.com/paper/2201.09851/) (2022)
- [Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings](https://mlsanity.com/paper/2103.10614/) (2021)
- [Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization](https://mlsanity.com/paper/2209.11979/) (2022)
- [Rethinking Coupled Tensor Analysis for Hyperspectral Super-Resolution: Recoverable Modeling Under Endmember Variability](https://mlsanity.com/paper/2512.19489/) (2025)
- [Subspace-Based Feature Fusion From Hyperspectral And Multispectral Image For Land Cover Classification](https://mlsanity.com/paper/2102.11228/) (2021)
- [Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition](https://mlsanity.com/paper/2310.10044/) (2023)
- [Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction](https://mlsanity.com/paper/2403.09096/) (2024)
- [A New Tensor Network: Tubal Tensor Train and Its Applications](https://mlsanity.com/paper/2603.10503/) (2026)
- [Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning](https://mlsanity.com/paper/2603.29438/) (2026)

## Abstract (arXiv)

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.