Feature registration of large resolution difference non-homologous SAR image pairs for sea ice drift tracking

2021 ◽  
Author(s):  
Peng Men ◽  
Hao Guo ◽  
Jubai An ◽  
Guanyu Li
Keyword(s):  
Sea Ice ◽  
Sar Image ◽  
Ice Drift ◽  
Feature Registration ◽  
Sea Ice Drift ◽  
Image Pairs

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.

scholarly journals Sea ice drift from Sentinel-1 SAR imagery using open source feature tracking

2015 ◽  
Vol 9 (6) ◽  
pp. 6937-6959 ◽  
Author(s):  
S. Muckenhuber ◽  
A. Korosov ◽  
S. Sandven
Keyword(s):  
Sea Ice ◽  
Open Source ◽  
Feature Tracking ◽  
Tracking Algorithm ◽  
Test Image ◽  
Tracking Algorithms ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Sar Imagery ◽  
Image Pairs

Abstract. A computational efficient, open source feature tracking algorithm, called ORB, is adopted and tuned for sea ice drift retrieval from Sentinel-1 SAR images. The best suitable setting and parameter values have been found using four representative Sentinel-1 image pairs. A new quality measure for feature tracking algorithms is introduced utilising the distribution of the resulting vector field. The performance of the algorithm is compared with two other feature tracking algorithms (SIFT and SURF). Applied on a test image pair acquired over Fram Strait, the tuned ORB algorithm produces the highest number of vectors (6920, SIFT: 1585 and SURF: 518) while being computational most efficient (66 s, SIFT: 182 s and SURF: 99 s using a 2,7 GHz processor with 8 GB memory). For validation purpose, 350 manually drawn vectors have been compared with the closest calculated vectors and the resulting root mean square distance is 609.9 m (equivalent to 7.5 pixel). All test image pairs show a significant better performance of the HV channel. On average, around 4 times more vectors have been found using HV polarisation. All software requirements necessary for applying the presented feature tracking algorithm are open source to ensure a free and easy implementation.

scholarly journals Open-source sea ice drift algorithm for Sentinel-1 SAR imagery using a combination of feature tracking and pattern matching

2017 ◽  
Vol 11 (4) ◽  
pp. 1835-1850 ◽  
Author(s):  
Stefan Muckenhuber ◽  
Stein Sandven
Keyword(s):  
Sea Ice ◽  
Open Source ◽  
Pattern Matching ◽  
Feature Tracking ◽  
Efficient Computation ◽  
Drift Field ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Sar Imagery ◽  
Image Pairs

Abstract. An open-source sea ice drift algorithm for Sentinel-1 SAR imagery is introduced based on the combination of feature tracking and pattern matching. Feature tracking produces an initial drift estimate and limits the search area for the consecutive pattern matching, which provides small- to medium-scale drift adjustments and normalised cross-correlation values. The algorithm is designed to combine the two approaches in order to benefit from the respective advantages. The considered feature-tracking method allows for an efficient computation of the drift field and the resulting vectors show a high degree of independence in terms of position, length, direction and rotation. The considered pattern-matching method, on the other hand, allows better control over vector positioning and resolution. The preprocessing of the Sentinel-1 data has been adjusted to retrieve a feature distribution that depends less on SAR backscatter peak values. Applying the algorithm with the recommended parameter setting, sea ice drift retrieval with a vector spacing of 4 km on Sentinel-1 images covering 400 km  ×  400 km, takes about 4 min on a standard 2.7 GHz processor with 8 GB memory. The corresponding recommended patch size for the pattern-matching step that defines the final resolution of each drift vector is 34  ×  34 pixels (2.7  ×  2.7 km). To assess the potential performance after finding suitable search restrictions, calculated drift results from 246 Sentinel-1 image pairs have been compared to buoy GPS data, collected in 2015 between 15 January and 22 April and covering an area from 80.5 to 83.5° N and 12 to 27° E. We found a logarithmic normal distribution of the displacement difference with a median at 352.9 m using HV polarisation and 535.7 m using HH polarisation. All software requirements necessary for applying the presented sea ice drift algorithm are open-source to ensure free implementation and easy distribution.

scholarly journals Open-source sea ice drift algorithm for Sentinel-1 SAR imagery using a combination of feature-tracking and pattern-matching

2016 ◽  
Author(s):  
Stefan Muckenhuber ◽  
Stein Sandven
Keyword(s):  
Sea Ice ◽  
Open Source ◽  
Pattern Matching ◽  
Feature Tracking ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Logarithmic Distribution ◽  
Sar Imagery ◽  
Image Pairs ◽  
Representative Image

Abstract. An open-source sea ice drift algorithm for Sentinel-1 SAR imagery is introduced based on the combination of feature-tracking and pattern-matching. A computational efficient feature-tracking algorithm produces an initial drift estimate and limits the search area for the pattern-matching, that provides small to medium scale drift adjustments and normalised cross correlation values as quality measure. The algorithm is designed to utilise the respective advantages of the two approaches and allows drift calculation at user defined locations. The pre-processing of the Sentinel-1 data has been optimised to retrieve a feature distribution that depends less on SAR backscatter peak values. A recommended parameter set for the algorithm has been found using a representative image pair over Fram Strait and 350 manually derived drift vectors as validation. Applying the algorithm with this parameter setting, sea ice drift retrieval with a vector spacing of 8 km on Sentinel-1 images covering 400 km x 400 km, takes less than 3.5 minutes on a standard 2.7 GHz processor with 8 GB memory. For validation, buoy GPS data, collected in 2015 between 15th January and 22nd April and covering an area from 81° N to 83.5° N and 12° E to 27° E, have been compared to calculated drift results from 261 corresponding Sentinel-1 image pairs. We found a logarithmic distribution of the error with a peak at 300 m. All software requirements necessary for applying the presented sea ice drift algorithm are open-source to ensure free implementation and easy distribution.

scholarly journals Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift

2016 ◽  
Vol 8 (5) ◽  
pp. 397 ◽  
Author(s):  
Yufang Ye ◽  
Mohammed Shokr ◽  
Georg Heygster ◽  
Gunnar Spreen
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
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 Method to characterize directional changes in Arctic sea ice drift and associated deformation due to synoptic atmospheric variations using Lagrangian dispersion statistics

2017 ◽  
Vol 11 (4) ◽  
pp. 1707-1731 ◽  
Author(s):  
Jennifer V. Lukovich ◽  
Cathleen A. Geiger ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Single Particle ◽  
Weather Forecasting ◽  
Particle Dispersion ◽  
Arctic Sea Ice ◽  
Maximum Pressure ◽  
Dynamical Regime ◽  
Scaling Exponent ◽  
Ice Drift ◽  
Sea Ice Drift

Abstract. A framework is developed to assess the directional changes in sea ice drift paths and associated deformation processes in response to atmospheric forcing. The framework is based on Lagrangian statistical analyses leveraging particle dispersion theory which tells us whether ice drift is in a subdiffusive, diffusive, ballistic, or superdiffusive dynamical regime using single-particle (absolute) dispersion statistics. In terms of sea ice deformation, the framework uses two- and three-particle dispersion to characterize along- and across-shear transport as well as differential kinematic parameters. The approach is tested with GPS beacons deployed in triplets on sea ice in the southern Beaufort Sea at varying distances from the coastline in fall of 2009 with eight individual events characterized. One transition in particular follows the sea level pressure (SLP) high on 8 October in 2009 while the sea ice drift was in a superdiffusive dynamic regime. In this case, the dispersion scaling exponent (which is a slope between single-particle absolute dispersion of sea ice drift and elapsed time) changed from superdiffusive (α ∼ 3) to ballistic (α ∼ 2) as the SLP was rounding its maximum pressure value. Following this shift between regimes, there was a loss in synchronicity between sea ice drift and atmospheric motion patterns. While this is only one case study, the outcomes suggest similar studies be conducted on more buoy arrays to test momentum transfer linkages between storms and sea ice responses as a function of dispersion regime states using scaling exponents. The tools and framework developed in this study provide a unique characterization technique to evaluate these states with respect to sea ice processes in general. Application of these techniques can aid ice hazard assessments and weather forecasting in support of marine transportation and indigenous use of near-shore Arctic areas.

Sign in / Sign up

Export Citation Format

Share Document