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http://dx.doi.org/10.7780/kjrs.2020.36.5.2.10

A Pansharpening Algorithm of KOMPSAT-3A Satellite Imagery by Using Dilated Residual Convolutional Neural Network  

Choi, Hoseong (Department of Civil Engineering, Chungbuk National University)
Seo, Doochun (Satellite Information Center, Korea Aerospace Research Institute)
Choi, Jaewan (Department of Civil Engineering, Chungbuk National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_2, 2020 , pp. 961-973 More about this Journal
Abstract
In this manuscript, a new pansharpening model based on Convolutional Neural Network (CNN) was developed. Dilated convolution, which is one of the representative convolution technologies in CNN, was applied to the model by making it deep and complex to improve the performance of the deep learning architecture. Based on the dilated convolution, the residual network is used to enhance the efficiency of training process. In addition, we consider the spatial correlation coefficient in the loss function with traditional L1 norm. We experimented with Dilated Residual Networks (DRNet), which is applied to the structure using only a panchromatic (PAN) image and using both a PAN and multispectral (MS) image. In the experiments using KOMPSAT-3A, DRNet using both a PAN and MS image tended to overfit the spectral characteristics, and DRNet using only a PAN image showed a spatial resolution improvement over existing CNN-based models.
Keywords
Convolutional Neural Network (CNN); Dilated Residual Network; KOMPSAT-3A; Pansharpening; Spatial Correlation Coefficient;
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Times Cited By KSCI : 4  (Citation Analysis)
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