PanNet: A Deep Network Architecture for Pan-Sharpening
Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, John Paisley
Abstract
We propose a deep network architecture for the pan-sharpening 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 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.
BibTeX
@inproceedings{iccv2017_pannetadeepnetwo,
title = {PanNet: A Deep Network Architecture for Pan-Sharpening},
author = {Junfeng Yang and Xueyang Fu and Yuwen Hu and Yue Huang and Xinghao Ding and John Paisley},
booktitle = {ICCV 2017},
year = {2017}
}