首页|A generative adversarial network with joint multistream architecture and spectral compensation for pansharpening
A generative adversarial network with joint multistream architecture and spectral compensation for pansharpening
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NSTL
Elsevier
Convolutional neural networks (CNNs) and variational models for pansharpening have obtained a compelling performance gain over the state of the art. Inspired by these models, we propose MSCGAN, a generative adversarial network (GAN) with joint multistream architecture and spectral compensation for pansharpening that uses a variational model to incorporate domain-specific knowledge and in particular, introduces a spectral compensation block. First, we extract the structural information of the panchromatic (PAN) image and input it into the generator together with the upsampled multispectral (MS) image. Then, we design a multistream pansharpening CNN architecture suite for domain-specific knowledge. Second, to boost the quality of the pansharpened images, we put the MS image in the generator and design a spectral compensation block. Then, we introduce the concept of the energy function of the variational model and add corresponding spectral constraints and spatial structure constraints to the objective function to achieve a compromise between spectral information fidelity and spatial information fidelity. Finally, the discriminator also introduces spatial structure information to help the generator generate the desired high-resolution multispectral image. Experiments on the images captured by the Quickbird and WorldView-2 satellites show that the our proposed MSCGAN can make use of PAN and LRMS images adequately to attain very encouraging results, obtaining large gains over the state of the art both visually and as measured by quality metrics. (c) 2022 Elsevier B.V. All rights reserved.
NSTL
Elsevier
