Learning deep multiresolution representations for pansharpening

TL;DR

This paper introduces a pyramid-based deep learning framework for pansharpening that effectively preserves spectral and spatial details across multiple scales, outperforming existing methods.

Contribution

A novel multiresolution deep fusion architecture that maintains spectral and spatial information at different scales through shared parameters and residual learning.

Findings

Outperforms state-of-the-art pansharpening models.

Effectively preserves spectral and spatial characteristics.

Provides publicly available code and dataset.

Abstract

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.