PCDRN: Progressive Cascade Deep Residual Network for Pansharpening

Remote Sensing

Wei Tu¹  Hangyuan Lu¹  Yong Yang¹  Shuying Huang² 

[1] School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China;School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China;

关键词: pansharpening;    deep residual network;    loss function;   

DOI  :  10.3390/rs12040676

来源: DOAJ

【 摘 要 】

Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pléiades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments.

【 授权许可】

Unknown