PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images
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TLDR
In this article, a universal spectral super-resolution network based on physical optimization unfolding for arbitrary multispectral images, including single-resolution and cross-scale multi-spectral images was proposed.About:
This article is published in Information Fusion.The article was published on 2022-04-01 and is currently open access. It has received 22 citations till now. The article focuses on the topics: Multispectral image & Hyperspectral imaging.read more
Citations
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Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer
TL;DR: Zhou et al. as mentioned in this paper proposed a feature interpolation module that deeply couples optical flow and multi-scale deformable convolution to predict unknown frames to enhance the spatial and temporal resolution of satellite video.
Journal ArticleDOI
DsTer: A dense spectral transformer for remote sensing spectral super-resolution
TL;DR: Wang et al. as discussed by the authors proposed a dense spectral transformer with ResNet to achieve spectral super-resolution for multispectral remote sensing images, which meets the need for 3D data handling and learning long-range relationships.
Journal ArticleDOI
Generating a long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS).
TL;DR: In this paper , a deep learning-based framework (DeepCAMS) was developed to downscale CAMS PM2.5 product on the spatiotemporal dimension for resolution enhancement, which can be learned from the high quality (0.25°, hourly) but short-term (2018-2020) Goddard Earth Observing System composition forecast (GEOS-CF) system.
Journal ArticleDOI
An attention mechanism based convolutional network for satellite precipitation downscaling over China
TL;DR: In this article , an attention mechanism based convolutional network (AMCN) is proposed to downscale GPM IMERG monthly precipitation data from 0.1° to 0.01°.
Journal ArticleDOI
From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution
TL;DR: Zhou et al. as mentioned in this paper proposed a self-supervised degradation-guided adaptive network to mitigate the domain gap between simulation and reality, which incorporated contrastive learning to blind remote sensing image SR, which guided the reconstruction process by encouraging the positive representations (relevant information) while punishing the negatives.
References
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