15 May 2018 Pansharpening by interspectral similarity and edge information using improved deep residual network
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Abstract
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.
© 2018 SPIE and IS&T
Peng-Yu Chen, Shen-Chuan Tai, "Pansharpening by interspectral similarity and edge information using improved deep residual network," Journal of Electronic Imaging 27(3), 033013 (15 May 2018). https://doi.org/10.1117/1.JEI.27.3.033013 Submission: Received 25 December 2017; Accepted 18 April 2018
Submission: Received 25 December 2017; Accepted 18 April 2018
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