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Open AccessArticle

Sentinel-2 Sharpening via Parallel Residual Network

by Jiemin Wu, Zhi He * and Jie Hu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 279;
Received: 18 December 2019 / Revised: 8 January 2020 / Accepted: 10 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Image Super-Resolution in Remote Sensing)
Sentinel-2 data is of great utility for a wide range of remote sensing applications due to its free access and fine spatial-temporal coverage. However, restricted by the hardware, only four bands of Sentinel-2 images are provided at 10 m resolution, while others are recorded at reduced resolution (i.e., 20 m or 60 m). In this paper, we propose a parallel residual network for Sentinel-2 sharpening termed SPRNet, to obtain the complete data at 10 m resolution. The proposed network aims to learn the mapping between the low-resolution (LR) bands and ideal high-resolution (HR) bands by three steps, including parallel spatial residual learning, spatial feature fusing and spectral feature mapping. First, rather than using the single branch network, the parallel residual learning structure is proposed to extract the spatial features from different resolution bands separately. Second, the spatial feature fusing is aimed to fully fuse the extracted features from each branch and produce the residual image with spatial information. Third, to keep spectral fidelity, the spectral feature mapping is utilized to directly propagate the spectral characteristics of LR bands to target HR bands. Without using extra training data, the proposed network is trained with the lower scale data synthesized from the observed Sentinel-2 data and applied to the original ones. The data at 10 m spatial resolution can be finally obtained by feeding the original 10 m, 20 m and 60 m bands to the trained SPRNet. Extensive experiments conducted on two datasets indicate that the proposed SPRNet obtains good results in the spatial fidelity and the spectral preservation. Compared with the competing approaches, the SPRNet increases the SRE by at least 1.538 dB on 20 m bands and 3.188 dB on 60 m bands while reduces the SAM by at least 0.282 on 20 m bands and 0.162 on 60 m bands. View Full-Text
Keywords: image sharpening; residual learning; parallel structure; convolution neural network; Sentinel-2 image sharpening; residual learning; parallel structure; convolution neural network; Sentinel-2
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Wu, J.; He, Z.; Hu, J. Sentinel-2 Sharpening via Parallel Residual Network. Remote Sens. 2020, 12, 279.

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