Towards breaking the spatial resolution barriers: An optical flow and super-resolution approach for sea ice motion estimation

Petrou, Zisis I.; Xian, Yang

doi:10.1016/j.isprsjprs.2018.01.020

Cited by 18 publications

(18 citation statements)

References 43 publications

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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%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower ds are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-ds group and the Hard-ds group, respectively.

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Section: Ice Motionmentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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“…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%

Parameter Estimation and Motion Tracking of Pack Ice From Fy-3/Mersi Images

Zhou

Zhang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. A method to estimate the shape parameters and track the motion velocity of pack ice is presented. The method consists of sea ice recognition, pack ice extraction, parameters estimation and motion tracking. We try to estimate the shape parameters and track the motion velocity from FY-3/MERSI images of Fram strait, which were covered by cloud less than 30%. It found that the pack ice concentration significantly decreases faster than sea ice concentration. The two parameters indicate the distribution difference between sea ices and pack ices. The change of the perimeter and mean clamp diameter are obvious owing to they were sensitive to the edge of the pack ices. But the small change of roundness, convexity and width-height ratio indicate the shape similarity of pack ices in various size. More than 80% of the pack ices in the study region are tracked successfully by the proposed method. The resulting motion vectors in the overlapping zones are found to match well with the polar daily grid sea ice motion vectors provided by the National Snow and Ice Data Centre. The method can provide the actual motion vector of pack ices to improve the distribution and details of the grid-to-grid motion vectors.

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“…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%

Comparison of Multi-Images Deep Learning Super Resolution for Passive Microwave Images of Arctic Sea Ice

Shen

Liu

Yao

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice.

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Towards breaking the spatial resolution barriers: An optical flow and super-resolution approach for sea ice motion estimation

Cited by 18 publications

References 43 publications

Object Tracking Based on Satellite Videos: A Literature Review

Object Tracking Based on Satellite Videos: A Literature Review

Parameter Estimation and Motion Tracking of Pack Ice From Fy-3/Mersi Images

Comparison of Multi-Images Deep Learning Super Resolution for Passive Microwave Images of Arctic Sea Ice

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