Feature tracking for sea ice drift retrieval from SAR images

2017 ◽  
Author(s):  
Denis Demchev ◽  
Vladimir Volkov ◽  
Eduard Kazakov ◽  
Stein Sandven
Keyword(s):  
Sea Ice ◽  
Feature Tracking ◽  
Sar Images ◽  
Ice Drift ◽  
Sea Ice Drift

Oceanic eddy signature on SAR-derived sea ice drift and vorticity

2021 ◽  
Author(s):  
Angelina Cassianides ◽  
Camillie Lique ◽  
Anton Korosov
Keyword(s):  
Sea Ice ◽  
Surface Current ◽  
Satellite Observation ◽  
The Arctic ◽  
Global Ocean ◽  
Sar Images ◽  
Surface Ocean ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Oceanic Eddy

<p>In the global ocean, mesoscale eddies are routinely observed from satellite observation. In the Arctic Ocean, however, their observation is impeded by the presence of sea ice, although there is a growing recognition that eddy may be important for the evolution of the sea ice cover. In this talk, we will present a new method of surface ocean eddy detection based on their signature in sea ice vorticity retrieved from Synthetic Aperture Radar (SAR) images. A combination of Feature Tracking and Pattern Matching algorithm is used to compute the sea ice drift from pairs of SAR images. We will mostly focus on the case of one eddy in October 2017 in the marginal ice zone of the Canadian Basin, which was sampled by mooring observations, allowing a detailed description of its characteristics. Although the eddy could not be identified by visual inspection of the SAR images, its signature is revealed as a dipole anomaly in sea ice vorticity, which suggests that the eddy is a dipole composed of a cyclone and an anticyclone, with a horizontal scale of 80-100 km and persisted over a week. We will also discuss the relative contributions of the wind and the surface current to the sea ice vorticity. We anticipate that the robustness of our method will allow us to detect more eddies as more SAR observations become available in the future.</p>

scholarly journals Operational SAR-based sea ice drift monitoring over the Baltic Sea

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 473-483 ◽  
Author(s):  
J. Karvonen
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Vector Fields ◽  
Phase Correlation ◽  
The Baltic Sea ◽  
Sar Images ◽  
Envisat Asar ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
The Baltic

Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR-based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season, and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.

scholarly journals Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results

2011 ◽  
Vol 52 (57) ◽  
pp. 311-317 ◽  
Author(s):  
Thomas Hollands ◽  
Wolfgang Dierking
Keyword(s):  
Sea Ice ◽  
Displacement Vector ◽  
Winter Season ◽  
Image Resolution ◽  
Displacement Fields ◽  
Sar Images ◽  
High Entropy ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Ice Conditions

AbstractSea-ice drift fields were obtained from sequences of synthetic aperture radar (SAR) images using a method based on pattern recognition. the accuracy of the method was estimated for two image products of the Envisat Advanced SAR (ASAR) with 25 m and 150 m pixel size. For data from the winter season it was found that 99% of the south–north and west–east components of the determined displacement vector are within ±3–5 pixels of a manually derived reference dataset, independent of the image resolution. For an image pair with 25 m resolution acquired during summer, the corresponding value is 12 pixels. Using the same resolution cell dimensions for the displacement fields in both image types, the estimated displacement components differed by 150–300 m. the use of different texture parameters for predicting the performance of the algorithm dependent on ice conditions and image characteristics was studied. It was found that high entropy values indicate a good performance.

Combined Feature-Tracking and Pattern-Matching Algorithm for Sea-Ice Drift Detection

2019 ◽  
Vol 56 (16) ◽  
pp. 161005
Author(s):  
王军凯 Junkai Wang ◽  
吕晓琪 Xiaoqi Lü ◽  
张明 Ming Zhang ◽  
李菁 Jing Li ◽  
孟娴静 Xianjing Meng ◽  
...  
Keyword(s):  
Sea Ice ◽  
Pattern Matching ◽  
Feature Tracking ◽  
Matching Algorithm ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Pattern Matching Algorithm ◽  
Combined Feature

Sea Ice Drift Tracking From Sequential SAR Images Using Accelerated-KAZE Features

2017 ◽  
Vol 55 (9) ◽  
pp. 5174-5184 ◽  
Author(s):  
Denis Demchev ◽  
Vladimir Volkov ◽  
Eduard Kazakov ◽  
Pablo F. Alcantarilla ◽  
Stein Sandven ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sar Images ◽  
Ice Drift ◽  
Sea Ice Drift

scholarly journals Enhanced Delaunay Triangulation Sea Ice Tracking Algorithm with Combining Feature Tracking and Pattern Matching

2020 ◽  
Vol 12 (3) ◽  
pp. 581 ◽  
Author(s):  
Ming Zhang ◽  
Jubai An ◽  
Jie Zhang ◽  
Dahua Yu ◽  
Junkai Wang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Pattern Matching ◽  
Delaunay Triangulation ◽  
Feature Tracking ◽  
Feature Detection ◽  
Global Climate ◽  
Retrieval Algorithm ◽  
Speeded Up Robust Features ◽  
Ice Drift ◽  
Sea Ice Drift

Sea ice drift detection has the key role of global climate analysis and waterway planning. The ability to detect sea ice drift in real-time also contributes to the safe navigation of ships and the prevention of offshore oil platform accidents. In this paper, an Enhanced Delaunay Triangulation (EDT) algorithm for sea ice tracking was proposed for dual-polarization sequential Synthetic Aperture Radar (SAR) images, which was implemented by combining feature tracking with pattern matching based on integrating HH and HV polarization feature information. A sea ice retrieval algorithm for feature detection, matching, fusion, and outlier detection was specifically developed to increase the system’s accuracy and robustness. In comparison with several state-of-the-art sea ice drift retrieval algorithms, including Speeded Up Robust Features (SURF) and the Oriented FAST and Rotated BRIEF (ORB) method, the results of the experiment provided compelling evidence that our algorithm had a higher accuracy than the SURF and ORB method. Furthermore, the results of our method were compared with the drift vector and direction of buoys data. The drift direction is consistent with buoys, and the velocity deviation was about 10 m. It was proved that this method can be applied effectively to the retrieval of sea ice drift.

scholarly journals Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery

2016 ◽  
Vol 10 (2) ◽  
pp. 913-925 ◽  
Author(s):  
Stefan Muckenhuber ◽  
Anton Andreevich Korosov ◽  
Stein Sandven
Keyword(s):  
Sea Ice ◽  
Open Source ◽  
Feature Tracking ◽  
Tracking Algorithm ◽  
Test Image ◽  
Speeded Up Robust Features ◽  
Radar Images ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Image Pairs

Abstract. A computationally efficient, open-source feature-tracking algorithm, called ORB, is adopted and tuned for sea ice drift retrieval from Sentinel-1 SAR (Synthetic Aperture Radar) images. The most suitable setting and parameter values have been found using four Sentinel-1 image pairs representative of sea ice conditions between Greenland and Severnaya Zemlya during winter and spring. The performance of the algorithm is compared to two other feature-tracking algorithms, namely SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features). Having been applied to 43 test image pairs acquired over Fram Strait and the north-east of Greenland, the tuned ORB (Oriented FAST and Rotated BRIEF) algorithm produces the highest number of vectors (177 513, SIFT: 43 260 and SURF: 25 113), while being computationally most efficient (66 s, SIFT: 182 s and SURF: 99 s per image pair using a 2.7 GHz processor with 8 GB memory). For validation purposes, 314 manually drawn vectors have been compared with the closest calculated vectors, and the resulting root mean square error of ice drift is 563 m. All test image pairs show a significantly better performance of the HV (horizontal transmit, vertical receive) channel due to higher informativeness. On average, around four times as many vectors have been found using HV polarization. All software requirements necessary for applying the presented feature-tracking algorithm are open source to ensure a free and easy implementation.

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