Generative Adversarial Network for Pansharpening With Spectral and Spatial Discriminators
| dc.rights.license | open | en_US |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | GASTINEAU, Anais | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | AUJOL, Jean-Francois
IDREF: 081164432 | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | BERTHOUMIEU, Yannick | |
| hal.structure.identifier | Laboratoire de l'intégration, du matériau au système [IMS] | |
| dc.contributor.author | GERMAIN, Christian
ORCID: 0000-0002-3097-8283 IDREF: 130936634 | |
| dc.date.accessioned | 2022-07-05T13:02:38Z | |
| dc.date.available | 2022-07-05T13:02:38Z | |
| dc.date.issued | 2022-01 | |
| dc.identifier.issn | 0196-2892 | en_US |
| dc.identifier.uri | oai:crossref.org:10.1109/tgrs.2021.3060958 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/140368 | |
| dc.description.abstractEn | The pansharpening problem amounts to fusing a high-resolution panchromatic image with a low-resolution multispectral image so as to obtain a high-resolution multispectral image. Therefore, the preservation of the spatial resolution of the panchromatic image and the spectral resolution of the multispectral image is of key importance for the pansharpening problem. To cope with it, we propose a new method based on a bidiscriminator in a generative adversarial network (GAN) framework. The first discriminator is optimized to preserve textures of images by taking as input the luminance and the near-infrared band of images, and the second discriminator preserves the color by comparing the chroma components Cb and Cr. Thus, this method allows to train two discriminators, each one with a different and complementary task. Moreover, to enhance these aspects, the proposed method based on bidiscriminator, and called MDSSC-GAN SAM, considers a spatial and a spectral constraint in the loss function of the generator. We show the advantages of this new method on experiments carried out on Pléiades and World View 3 satellite images. | |
| dc.description.sponsorship | Super-résolution d'images multi-échelles en sciences des matériaux avec des attributs géométriques - ANR-18-CE92-0050 | en_US |
| dc.language.iso | EN | en_US |
| dc.source | crossref | |
| dc.subject.en | Bidiscriminator | |
| dc.subject.en | deep learning | |
| dc.subject.en | generative adversarial network (GAN) | |
| dc.subject.en | pansharpening | |
| dc.subject.en | remote sensing | |
| dc.title.en | Generative Adversarial Network for Pansharpening With Spectral and Spatial Discriminators | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1109/tgrs.2021.3060958 | en_US |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Electronique | en_US |
| bordeaux.journal | IEEE Transactions on Geoscience and Remote Sensing | en_US |
| bordeaux.page | 1-11 | en_US |
| bordeaux.volume | 60 | en_US |
| bordeaux.hal.laboratories | Laboratoire d’Intégration du Matériau au Système (IMS) - UMR 5218 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | Bordeaux INP | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | dissemin | |
| hal.export | false | |
| workflow.import.source | dissemin | |
| dc.rights.cc | Pas de Licence CC | en_US |
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