LAUSR

Although the multiresolution analysis (MRA)-based pansharpening methods are able to generate high-resolution multispectral (HRMS) images with good spectral retention, they are prone to spatial distortion. To address this problem, this letter combines deep learning (DL) with the MRA methods and proposes a novel detail-injected-based multiscale asymmetric residual network. The difference strategy is combined with MRA methods and the corresponding injection coefficients are obtained using the multiscale residual block (MSRB) to effectively map spatial information to each waveband of the multispectral (MS) images. In addition, asymmetric convolution block (ACB) is embedded in the residual network to obtain more robust features, and an inception feature pyramid network (FPN) is designed to enrich the spatial information of the fusion results while fusing features at different levels. Experimental results show that the method proposed in this letter outperforms the state-of-the-art pansharpening methods.