# A Generative Model Method for Unsupervised Multispectral Image Fusion in Remote Sensing

arXiv: https://arxiv.org/abs/2102.03908 · HTML: https://mlsanity.com/paper/2102.03908/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [ChMusic: A Traditional Chinese Music Dataset for Evaluation of Instrument Recognition](https://mlsanity.com/paper/2108.08470/) (2021)

## Abstract (arXiv)

This paper presents a generative model method for multispectral image fusion in remote sensing which is trained without supervision. This method eases the supervision of learning and it also considers a multi-objective loss function to achieve image fusion. The loss function incorporates both spectral and spatial distortions. Two discriminators are designed to minimize the spectral and spatial distortions of the generative output. Extensive experimentations are conducted using three public domain datasets. The comparison results across four reduced-resolution and three full-resolution objective metrics show the superiority of the developed method over several recently developed methods.