# Learning deep multiresolution representations for pansharpening

arXiv: https://arxiv.org/abs/2102.08423 · HTML: https://mlsanity.com/paper/2102.08423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Hyperspectral pansharpening: a review](https://mlsanity.com/paper/1504.04531/) (2015)
- [Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery](https://mlsanity.com/paper/2005.08752/) (2020)
- [PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening](https://mlsanity.com/paper/1805.03371/) (2018)
- [Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network](https://mlsanity.com/paper/1705.07556/) (2017)
- [Target-adaptive CNN-based pansharpening](https://mlsanity.com/paper/1709.06054/) (2017)
- [PGMAN: An Unsupervised Generative Multi-adversarial Network for Pan-sharpening](https://mlsanity.com/paper/2012.09054/) (2020)
- [Unsupervised Pansharpening Based on Self-Attention Mechanism](https://mlsanity.com/paper/2006.09303/) (2020)
- [Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs](https://mlsanity.com/paper/2006.16644/) (2020)

## Abstract (arXiv)

Retaining spatial characteristics of panchromatic image and spectral information of multispectral bands is a critical issue in pansharpening. This paper proposes a pyramid based deep fusion framework that preserves spectral and spatial characteristics at different scales. The spectral information is preserved by passing the corresponding low resolution multispectral image as residual component of the network at each scale. The spatial information is preserved by training the network at each scale with the high frequencies of panchromatic image alongside the corresponding low resolution multispectral image. The parameters of different networks are shared across the pyramid in order to add spatial details consistently across scales. The parameters are also shared across fusion layers within a network at a specific scale. Experiments suggest that the proposed architecture outperforms state of the art pansharpening models. The proposed model, code and dataset is publicly available at https://github.com/sohaibali01/deep_pyramid_fusion.