2018
DOI: 10.1016/j.isprsjprs.2018.01.020
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Towards breaking the spatial resolution barriers: An optical flow and super-resolution approach for sea ice motion estimation

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Cited by 19 publications
(18 citation statements)
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“…Traditional [4] 2017 MCC tracker with hybrid example-based super-resolution model [128] 2017 A faster cross-correlation based tracking with several updates [129] 2018 A optical-flow based tracking with super-resolution enhancement [130] 2019 A multi-step tracker for ice motion tracking [131] 2020 Rotation-invariant ice floe tracking [132] 2021 Integrating the cross-correlation with feature tracking [133] 2022 Integrating locally consistent flow field filtering with cross-correlation DL-based [134] 2019 An encoder-decoder network with LSTM to predict ice motion trajectory [135] 2021 A CNN model to predict the arctic sea ice motions [136] 2021 A multi-step machine learning approach to track icebergs…”
Section: Ice Motionmentioning
confidence: 99%
“…Traditional [4] 2017 MCC tracker with hybrid example-based super-resolution model [128] 2017 A faster cross-correlation based tracking with several updates [129] 2018 A optical-flow based tracking with super-resolution enhancement [130] 2019 A multi-step tracker for ice motion tracking [131] 2020 Rotation-invariant ice floe tracking [132] 2021 Integrating the cross-correlation with feature tracking [133] 2022 Integrating locally consistent flow field filtering with cross-correlation DL-based [134] 2019 An encoder-decoder network with LSTM to predict ice motion trajectory [135] 2021 A CNN model to predict the arctic sea ice motions [136] 2021 A multi-step machine learning approach to track icebergs…”
Section: Ice Motionmentioning
confidence: 99%
“…The resolution and accuracy of the sea ice motion obtained by this method depends on the choice of the template and search area. The other method, optical flow method, approximately estimates the true motion velocity field by the change of the pixel grayscale in the sequential images (Salvador and Long, 2003;Petrou et al, 2018). This method is sensitive to light conditions.…”
Section: Instructionmentioning
confidence: 99%
“…Data from a variety of satellite sensors, including optical satellite images, passive microwave images, and synthetic aperture radar (SAR) images have been employed to observe polar sea ice. Although optical images, such as Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectromete (MERIS), and Advanced Very High Resolution Radiometer (AVHRR), have high temporalspatial resolution, they are often contaminated by cloud, even no available images can be obtained due to poor atmospheric conditions (Petrou et al, 2018). SAR images, such as Sentinel-1 (Xian and Tian, 2017), have high spatial resolution, but limit to the small swath and low temporal resolution, resulting in mass data processing when producing Arctic sea ice characteristics.…”
Section: Introductionmentioning
confidence: 99%
“…Passive microwave images, such as the Special Sensor Microwave Imager (SSM/I) on the series of satellites of Defense Meteorological Satellite Program (DMSP), the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) on Aqua satellite of the National Aeronautics and Space Administration (NASA) Earth Observation System (EOS), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on the Global Change Observation Mission 1st -Water "SHIZUKU" (GCOM-W1), are important data sources for Arctic sea ice observation with the advantages of wide coverage, high temporal resolution, strong surface penetration ability and all-weather work (Petrou et al, 2018). Among them, AMSR2 is one of the representative passive microwave sensors that has been observing sea ice since 2012, it has more frequency bands and relatively high spatial resolution (Han and Kim, 2018).…”
Section: Introductionmentioning
confidence: 99%