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

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.

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

Laser & Optoelectronics Progress ◽

10.3788/lop56.161005 ◽

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

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

The Cryosphere ◽

10.5194/tc-10-913-2016 ◽

2016 ◽

Vol 10 (2) ◽

pp. 913-925 ◽

Cited By ~ 16

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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A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data

Remote Sensing ◽

10.3390/rs9030258 ◽

2017 ◽

Vol 9 (3) ◽

pp. 258 ◽

Cited By ~ 19

Author(s):

Anton Korosov ◽

Pierre Rampal

Keyword(s):

Sea Ice ◽

Pattern Matching ◽

Feature Tracking ◽

Ice Drift ◽

Sea Ice Drift ◽

Sar Data

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Combined pattern matching and feature tracking for Bohai Sea ice drift detection using Gaofen-4 imagery

International Journal of Remote Sensing ◽

10.1080/01431161.2020.1760396 ◽

2020 ◽

Vol 41 (19) ◽

pp. 7486-7508

Author(s):

Ruifu Wang ◽

Dan Huang ◽

Xi Zhang ◽

Pan Wei

Keyword(s):

Sea Ice ◽

Pattern Matching ◽

Feature Tracking ◽

Bohai Sea ◽

Ice Drift ◽

Sea Ice Drift

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

The Cryosphere ◽

10.5194/tc-11-1835-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1835-1850 ◽

Cited By ~ 9

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.

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

10.5194/tc-2016-261 ◽

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.

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Sea ice drift data for Fram Strait derived from a feature-tracking algorithm applied on Sentinel-1 SAR imagery

Data in Brief ◽

10.1016/j.dib.2018.04.034 ◽

2018 ◽

Vol 18 ◽

pp. 1410-1415 ◽

Cited By ~ 2

Author(s):

Stefan Muckenhuber ◽

Stein Sandven

Keyword(s):

Sea Ice ◽

Feature Tracking ◽

Tracking Algorithm ◽

Fram Strait ◽

Ice Drift ◽

Sea Ice Drift ◽

Sar Imagery

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

The Cryosphere Discussions ◽

10.5194/tcd-9-6937-2015 ◽

2015 ◽

Vol 9 (6) ◽

pp. 6937-6959 ◽

Cited By ~ 1

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.

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