TY - JOUR
T1 - Pansharpening by interspectral similarity and edge information using improved deep residual network
AU - Chen, Peng Yu
AU - Tai, Shen Chuan
N1 - Funding Information:
FORMOSA-2 images were acquired from the National Space Organization (NSPO) Taiwan. The authors would like to thank NSPO for technical support and data provision.
Publisher Copyright:
© 2018 SPIE and IS&T.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - Remote sensing image pansharpening involves the fusing of multispectral (MS) images with a panchromatic (PAN) image to produce an image with high-spatial as well as high-spectral resolution. We propose an improved pansharpening algorithm based on deep learning. A four-layer residual network is used as a reconstructed model to enable the accurate estimation of high-frequency details. We consider two priors to take advantage of MS information. The first prior indicates interspectral similarity, wherein the relationship between high- and low-resolution PAN images is used in the estimation of high-resolution MS images. The second prior provides the location of edges and textures according to the gradient of the PAN image. Consistency in the spectral characteristic is used as the basis in creating a pretrained model with the aim of accelerating convergence. Multiple evaluation metrics were applied to simulated and real images in order to compare the efficacy of the proposed method with that of state-of-the-art image fusion methods.
AB - Remote sensing image pansharpening involves the fusing of multispectral (MS) images with a panchromatic (PAN) image to produce an image with high-spatial as well as high-spectral resolution. We propose an improved pansharpening algorithm based on deep learning. A four-layer residual network is used as a reconstructed model to enable the accurate estimation of high-frequency details. We consider two priors to take advantage of MS information. The first prior indicates interspectral similarity, wherein the relationship between high- and low-resolution PAN images is used in the estimation of high-resolution MS images. The second prior provides the location of edges and textures according to the gradient of the PAN image. Consistency in the spectral characteristic is used as the basis in creating a pretrained model with the aim of accelerating convergence. Multiple evaluation metrics were applied to simulated and real images in order to compare the efficacy of the proposed method with that of state-of-the-art image fusion methods.
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U2 - 10.1117/1.JEI.27.3.033013
DO - 10.1117/1.JEI.27.3.033013
M3 - Article
AN - SCOPUS:85047720984
SN - 1017-9909
VL - 27
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
IS - 3
M1 - 033013
ER -