Detail Injection-Based Convolutional Auto-Encoder for Pansharpening

Abstract

The purpose of pansharpening is to generate high-resolution multispectral (MS) images using both low-resolution MS images and high-resolution panchromatic images. Traditional remote sensing image fusion algorithms can be simplified to a unified detail injection (Di) context that treats the injected MS details as panchromatic-detail and integration with injection gain. The injected details are developed from traditional fusion strategies with clear physical interpretation and facilitate fast convergence of deep learning models for high-quality image fusion. The excellent ability of convolutional autoencoder (CAE) networks to retain image information enables its application to remote sensing image fusion. In this paper, a fusion method Di-based CAE (DiCAE) based on Di and CAE is proposed. DiCAE method is based on Di as the theoretical foundation and CAE network as the core of the algorithm. In addition, our method is evaluated through experiments on different satellite datasets, and the fusion results obtained by DiCAE have better objective evaluation metrics and better visual results compared to other state-of-the-art methods.