Journal
NEUROCOMPUTING
Volume 312, Issue -, Pages 364-371Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.05.103
Keywords
Convolutional neural network; Pixel-wise regression; Remote sensing; Pansharpening
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Funding
- National Key R&D Program of China [2017YFC1501301]
- Natural Science Foundation of China [61673188, 61503144, 61761130081]
- Foundation for Innovative Research Groups of Hubei Province of China [2017CFA005]
- Fundamental Research Funds for the Central Universities [2017KFXKJC002]
- Natural Science Foundation of Hubei Province of China [2017CFB519]
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Convolutional neural networks are widely used for solving image recognition and other classification problems in which the whole image is considered as a single object. In this paper, we take the pansharpening problem of remote sensing images as an example to discuss how to establish pixel-wise regression models using convolutional neural networks. In order to meet the requirements of pixel-wise analysis on both the localization accuracy and the abstraction ability of the regression process, a U-shaped architecture is applied in our study to construct the network model. By establishing direct connections between convolution layers at the front end and the back end of the network, image features corresponding to different resolution levels can be retained. Then a regression relationship between these multi-resolution image features and the target image pixel values can be obtained. Experimental results show that the proposed regression model can effectively accomplish pansharpening, with better performance in controlling geometric deformation and color distortion, as compared to some state of the art methods. (C) 2018 Published by Elsevier B.V.
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