предыдущий|следующий

Deep learning-based super-resolution for GF-4 satellite imagery

  • role： First author第一作者
  • Affiliation：

    School of Geography and Planning, Center of Integrated Geographic Information Analysis, Sun Yat-sen University, Guangzhou 510275, China

    Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China

  • Email：hzhdhz@126.com
  • Introduction：贺智，1988年生，女，副教授，研究方向为高光谱遥感、特征提取、模式识别和深度学习。E-mail：hzhdhz@126.com
HE Zhi12,  
  • Affiliation：

    City College of Dongguan University of Technology, Dongguan 511700, China

HE Dan3

реферат

GF-4 is a geostationary orbit satellite launched under the support of the major special project of China’s high-resolution earth observation system. Notably, the spatial resolution of the medium-wave infrared channel in GF-4 satellite imagery is much lower than the corresponding visible near-infrared channels of the same scene. The resolution of the medium-wave infrared channel needs to be improved.In this study, a deep learning method termed as GF-4 Super-Resolution Network (GF-4-SRN) is proposed for super-resolution GF-4 satellite imagery. First, a convolutional regression network is designed for preliminary reconstruction of the original image. The convolutional regression network is trained using eight times down-sampled visible near-infrared channels and the original low-resolution medium-wave infrared channel. Thus, the correspondence between the visible near-infrared channels and the medium-wave infrared channel is developed. Second, a convolutional reconstruction network is designed to further reconstruct the preliminary results. This network is trained using the low-resolution preliminary reconstruction results and the original medium-wave infrared image. Notably, the convolutional reconstruction network can build the relationship between the preliminary reconstruction results and the medium-wave infrared image. Finally, the original visible near-infrared channels in GaoFen-4 satellite imagery are fed into the trained GF-4-SRN. The final super-resolution reconstruction results can be obtained by the relationship between visible near-infrared images and the medium-wave infrared image.Experiments are performed on two real-world GF-4 satellite images acquired from Hubei and Jiangxi regions. Experimental results demonstrate that the proposed GF-4-SRN method can effectively enhance the spatial resolution of the medium-wave infrared image. Compared with the root mean square error of state-of-the-art methods, that of the GF-4-SRN is reduced by at least 7.54. Moreover, the visual effects are much clearer and more natural. Therefore, the GF-4-SRN contributes to expanding the application range of the GF-4 satellite imagery.This paper proposes a deep learning-based super-resolution method for GF-4 satellite imagery. The proposed GF-4-SRN is composed of preliminary reconstruction by convolutional regression network and further reconstruction by convolutional reconstruction network. Experimental results on Hubei and Jiangxi regions demonstrate the effectiveness of the proposed GF-4-SRN. The spatial resolution is increased by fully utilizing the information provided by the visible near-infrared images. The root mean square error, erreur relative globale adimensionnelle de synthèse, and spectral angle mapper are lower than those of other methods. Meanwhile, the peak signal-to-noise ratio is higher than those of the competitors. Moreover, the visual effect of the results provided by the GF-4-SRN is much clearer and the reconstruction results contain more details. Notably, the deep learning-based method has great potential in super-resolution GF-4 satellite imagery.In the future, we will improve the GF-4-SRN by adopting various window sizes in the convolutional kernel. The network connection mode and optimization method can also be changed to improve the speed and performance of the network. The GF4-SRN will also be extended to other application areas, such as disaster monitoring.

ключеви́че слова́

GF-4;super-resolution;reconstruction;deep learning;convolutional networks;Hubei;Jiangxi

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