SDRCNN: A single-scale dense residual connected convolutional neural
  network for pansharpening
Yuan Fang Yuanzhi Cai Lei Fan
Pansharpening is a process of fusing a high spatial resolution panchromatic
image and a low spatial resolution multispectral image to create a
high-resolution multispectral image. A novel single-branch, single-scale
lightweight convolutional neural network, named SDRCNN, is developed in this
study. By using a novel dense residual connected structure and convolution
block, SDRCNN achieved a better trade-off between accuracy and efficiency. The
performance of SDRCNN was tested using four datasets from the WorldView-3,
WorldView-2 and QuickBird satellites. The compared methods include eight
traditional methods (i.e., GS, GSA, PRACS, BDSD, SFIM, GLP-CBD, CDIF and
LRTCFPan) and five lightweight deep learning methods (i.e., PNN, PanNet,
BayesianNet, DMDNet and FusionNet). Based on a visual inspection of the
pansharpened images created and the associated absolute residual maps, SDRCNN
exhibited least spatial detail blurring and spectral distortion, amongst all
the methods considered. The values of the quantitative evaluation metrics were
closest to their ideal values when SDRCNN was used. The processing time of
SDRCNN was also the shortest among all methods tested. Finally, the
effectiveness of each component in the SDRCNN was demonstrated in ablation
experiments. All of these confirmed the superiority of SDRCNN.