We propose a deep network architecture for the pansharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the reconstructed image. To preserve the spatial structure, we train our network parameters in the high-pass filtering domain rather than the image domain. We show that the trained network generalizes well to images from different satellites without needing retraining. Experiments show significant improvement over state-of-the-art methods visually and in terms of standard quality metrics.
Source: PanNet: A Deep Network Architecture for Pan-SharpeningPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Pansharpening | 3 | 50.00% |
Image Super-Resolution | 1 | 16.67% |
satellite image super-resolution | 1 | 16.67% |
Super-Resolution | 1 | 16.67% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |