Deep Learning-Based Detail Map Estimation for MultiSpectral Image Fusion in Remote Sensing

TL;DR

This paper introduces a deep learning method for estimating intensity components in multispectral image fusion, enhancing the resolution of multispectral images in remote sensing applications.

Contribution

It proposes a novel deep neural network approach to estimate high-resolution multispectral bands from low-resolution data, improving fusion performance.

Findings

Outperforms existing methods on three datasets

Uses joint multiplication of neighboring spectral bands

Achieves better objective metric scores

Abstract

This paper presents a deep learning-based estimation of the intensity component of MultiSpectral bands by considering joint multiplication of the neighbouring spectral bands. This estimation is conducted as part of the component substitution approach for fusion of PANchromatic and MultiSpectral images in remote sensing. After computing the band dependent intensity components, a deep neural network is trained to learn the nonlinear relationship between a PAN image and its nonlinear intensity components. Low Resolution MultiSpectral bands are then fed into the trained network to obtain an estimate of High Resolution MultiSpectral bands. Experiments conducted on three datasets show that the developed deep learning-based estimation approach provides improved performance compared to the existing methods based on three objective metrics.