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Algorithms 2019, 12(1), 16; https://doi.org/10.3390/a12010016

Learning an Efficient Convolution Neural Network for Pansharpening

Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Jiangsu Technology and Engineering Center of Meteorological Sensor Network, School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Received: 6 December 2018 / Revised: 21 December 2018 / Accepted: 26 December 2018 / Published: 8 January 2019
(This article belongs to the Special Issue Deep Learning for Image and Video Understanding)
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Abstract

Pansharpening is a domain-specific task of satellite imagery processing, which aims at fusing a multispectral image with a corresponding panchromatic one to enhance the spatial resolution of multispectral image. Most existing traditional methods fuse multispectral and panchromatic images in linear manners, which greatly restrict the fusion accuracy. In this paper, we propose a highly efficient inference network to cope with pansharpening, which breaks the linear limitation of traditional methods. In the network, we adopt a dilated multilevel block coupled with a skip connection to perform local and overall compensation. By using dilated multilevel block, the proposed model can make full use of the extracted features and enlarge the receptive field without introducing extra computational burden. Experiment results reveal that our network tends to induce competitive even superior pansharpening performance compared with deeper models. As our network is shallow and trained with several techniques to prevent overfitting, our model is robust to the inconsistencies across different satellites. View Full-Text
Keywords: pansharpening; convolutional neural network; nonlinear fusion model; dilated multilevel block; residual learning pansharpening; convolutional neural network; nonlinear fusion model; dilated multilevel block; residual learning
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Guo, Y.; Ye, F.; Gong, H. Learning an Efficient Convolution Neural Network for Pansharpening. Algorithms 2019, 12, 16.

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