UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion
TLDR
In this article, an unsupervised multi-attention-guided network named UMAG-Net was proposed to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral images (MSI) of the same scene.Abstract:
To reconstruct images with high spatial resolution and high spectral resolution, one of the most common methods is to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral image (MSI) of the same scene. Deep learning has been widely applied in the field of HSI-MSI fusion, which is limited with hardware. In order to break the limits, we construct an unsupervised multiattention-guided network named UMAG-Net without training data to better accomplish HSI-MSI fusion. UMAG-Net first extracts deep multiscale features of MSI by using a multiattention encoding network. Then, a loss function containing a pair of HSI and MSI is used to iteratively update parameters of UMAG-Net and learn prior knowledge of the fused image. Finally, a multiscale feature-guided network is constructed to generate an HR-HSI. The experimental results show the visual and quantitative superiority of the proposed method compared to other methods.read more
Citations
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MLR-DBPFN: A Multi-Scale Low Rank Deep Back Projection Fusion Network for Anti-Noise Hyperspectral and Multispectral Image Fusion
TL;DR: In this paper , a multiscale low-rank deep back projection fusion network (MLR-DBPFN) is proposed to fuse LR hyperspectral (HS) data and HR multispectral data.
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TL;DR: In this paper , a literature survey is conducted to analyze the trends of multimodal remote sensing data fusion, and some prevalent sub-fields in the field are reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic aperture radaroptical and RS-Geospatial Big Data fusion.
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