Generative Adversarial Network for Pansharpening with Spectral and Spatial Discriminators
| Autor: | Anais Gastineau, Jean-François Aujol, Christian Germain, Yannick Berthoumieu |
|---|---|
| Přispěvatelé: | Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'intégration, du matériau au système (IMS), Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1 |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Discriminator
Computer science Multispectral image 0211 other engineering and technologies 0207 environmental engineering pansharpening 02 engineering and technology Luminance Image (mathematics) remote sensing multi-discriminator Electrical and Electronic Engineering [MATH]Mathematics [math] 020701 environmental engineering Image resolution 021101 geological & geomatics engineering business.industry Pattern recognition Deep learning Function (mathematics) Panchromatic film Generative Adversarial Network General Earth and Planetary Sciences Artificial intelligence business Generator (mathematics) |
| Popis: | 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 Pleiades and World View 3 satellite images. |
| Databáze: | OpenAIRE |
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