Abstract

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.

Highlights

  • Sentinel-2 is a wide swath and optical fine resolution satellite imaging mission released by the European Space Agency (ESA) [1]

  • Since the 10 m version of LR bands are not available in the testing datasets, we follow the Wald’s protocol and give the quantitative evaluation at lower scale, i.e., the SPRNet2× is evaluated on the task to sharpen 40 m to 20 m; in the same way, the SPRNet6× is evaluated on the task to sharpen 360 m to 60 m

  • We propose a parallel residual network (i.e., SPRNet) for Sentinel-2 image sharpening to obtain complete data at the highest sensor resolution

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Summary

Introduction

Sentinel-2 is a wide swath and optical fine resolution satellite imaging mission released by the European Space Agency (ESA) [1]. Plenty of image sharpening methods have been proposed to enhance the spatial resolution of various sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS) [10], Advanced Spaceborne Theemal Emission and Reflection Radiometer (ASTER) [11], WorldView-2 [12] and more recently for Visible Infrared Imaging Radiometer Suite (VIIRS) [13] and Sentinel-2. These methods can be generally classified into three categories: classic pansharpening-based, model-based, and learning-based methods. The applicability of pansharpening-based methods is limited in Sentinel-2 sharpening

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