Laser & Optoelectronics Progress, Volume. 60, Issue 14, 1410013(2023)

Super-Resolution Reconstruction of FY-4 Images Based on Matching Extraction and Cross-Scale Feature Fusion Network

Zhengsong Lu1, Xi Kan2、*, Yan Li2, and Naiyuan Chen1
Author Affiliations
  • 1School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
  • 2School of the Internet of Thing Engineering, Wuxi University, Wuxi 214105, Jiangsu, China
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    References(34)

    [1] Gao H T, Bao S L, Liang H et al. Filtering algorithm for non lightning events using the FY-4 lightning mapping imager[J]. Acta Optica Sinica, 41, 0911001(2021).

    [2] Wang Q P, Zhu W N, Wang Y et al. Preliminary application of FY-4A satellite data in dense fog weather events at Urumqi international airport[J]. Meteorological Monthly, 47, 627-637(2021).

    [3] Li T, Wang L Y, Wang L et al. An inversion algorithm of Bohai Sea ice based on FY-4A remote sensing data[J]. Electronic Design Engineering, 30, 1-6(2022).

    [4] Zhang Y H, Cao H X, Kan X. Snow cover recognition for Xinjiang based on the fusion of FY-4A/AGRI spatial and temporal characteristics[J]. Remote Sensing Technology and Application, 35, 1337-1347(2020).

    [5] Qiao H W, Zhang Y L. FY-3C and FY-4A satellite data were combined to study the variation of snow cover area: a case study of Qilian Mountains[J]. Remote Sensing Technology and Application, 35, 1320-1328(2020).

    [6] Jiang H, He Q, Zeng X Q et al. Sand and dust monitoring using FY-4A satellite data based on the random forests and convolutional neural networks[J]. Plateau Meteorology, 40, 680-689(2021).

    [7] Kang J M, Sui L C, Li L et al. Super-resolution reconstruction of remote sensing images based on double-sparse K-SVD dictionary learning[J]. Computer Engineering and Applications, 54, 187-191(2018).

    [8] Zhou Z Y. Super-resolution reconstruction of remote sensing images by using empirical mode decomposition and compressed sensing[J]. Remote Sensing Technology and Application, 33, 96-102(2018).

    [9] Chan J C W, Ma J L, Kempeneers P et al. Superresolution enhancement of hyperspectral CHRIS/Proba images with a thin-plate spline nonrigid transform model[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 2569-2579(2010).

    [10] Galbraith A E, Theiler J, Thome K J et al. Resolution enhancement of multi-look imagery for the multispectral thermal imager[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 1964-1977(2005).

    [11] Ma J L, Chan J C W, Canters F. An Operational super⁃resolution approach for multi-temporal and multi-angle remotely sensed imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 110-124(2012).

    [12] Li F, Jia X P, Fraser D et al. Super-resolution for remote sensing images based on a universal hidden Markov tree model[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 1270-1278(2010).

    [13] Shen H F, Ng M K, Li P X et al. Super-resolution reconstruction algorithm to MODIS remote sensing images[J]. The Computer Journal, 52, 90-100(2007).

    [14] Banaras C, Bioucas-Dias J, Galliani S et al. Super-resolution of Sentinel-2 images: learning a globally applicable deep neural network[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319(2018).

    [15] Shi S, Yin Z S, Wang L. Dark channel and cross channel based multi-prior combined multi-spectral super-resolution algorithm[J]. Acta Optica Sinica, 42, 1010001(2022).

    [16] Dong C, Loy C C, He K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

    [17] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C], 1637-1645(2016).

    [18] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).

    [19] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

    [20] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. Leal-Taixé L, Roth S. Computer vision-ECCV 2018 workshops. Lecture notes in computer science, 11133, 63-79(2019).

    [21] Cheng D Q, Cai Y C, Chen L L et al. Multi-scale convolutional neural network reconstruction algorithm based on edge correction[J]. Laser & Optoelectronics Progress, 55, 091003(2018).

    [22] Gao Q Q, Zhao J W, Zhou Z H. Image super-resolution reconstruction based on recursive multi-scale convolutional networks[J]. Pattern Recognition and Artificial Intelligence, 33, 972-980(2020).

    [23] Xin Y X, Zhu F T, Shi P F et al. Super-resolution reconstruction algorithm of images based on improved enhanced super-resolution generative adversarial network[J]. Laser & Optoelectronics Progress, 59, 0420002(2022).

    [24] Pouliot D, Latifovic R, Pasher J et al. Landsat super-resolution enhancement using convolution neural networks and sentinel-2 for training[J]. Remote Sensing, 10, 394(2018).

    [25] Zhang K X, Sumbul G, Demir B. An approach to super-resolution of sentinel-2 images based on generative adversarial networks[C], 69-72(2020).

    [26] Xiao A R, Wang Z Y, Wang L et al. Super-resolution for Jilin-1 satellite video imagery via a convolutional network[J]. Sensors, 18, 1194(2018).

    [27] He Z, He D. Deep learning-based super-resolution for GF-4 satellite imagery[J]. Journal of Remote Sensing, 24, 1500-1510(2020).

    [28] Li L, Ni Z Y, Qi C L et al. Pre-launch radiometric calibration of geostationary interferometric infrared sounder on FengYun-4B satellite[J]. Acta Optica Sinica, 42, 0630001(2022).

    [30] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [32] Aggarwal H K, Majumdar A. Hyperspectral image denoising using spatio-spectral total variation[J]. IEEE Geoscience and Remote Sensing Letters, 13, 442-446(2016).

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    Zhengsong Lu, Xi Kan, Yan Li, Naiyuan Chen. Super-Resolution Reconstruction of FY-4 Images Based on Matching Extraction and Cross-Scale Feature Fusion Network[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1410013

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    Paper Information

    Category: Image Processing

    Received: Jul. 6, 2022

    Accepted: Sep. 13, 2022

    Published Online: Jul. 17, 2023

    The Author Email: Kan Xi (kanxi@nuist.edu.cn)

    DOI:10.3788/LOP222009

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