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

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.

Download Full-text

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

Download Full-text

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.

Download Full-text

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.

Download Full-text

Observation of Sea Ice Drift Characteristics with Marine Radar Images on an Offshore Platform in the Bohai Sea

Journal of Waterway Port Coastal and Ocean Engineering ◽

10.1061/(asce)ww.1943-5460.0000646 ◽

2021 ◽

Vol 147 (4) ◽

Author(s):

Xue Long ◽

Xiaodong Chen ◽

Anliang Wang ◽

Yu Liu ◽

Shunying Ji

Keyword(s):

Sea Ice ◽

Bohai Sea ◽

Offshore Platform ◽

The Bohai Sea ◽

Radar Images ◽

Ice Drift ◽

Sea Ice Drift ◽

Marine Radar

Download Full-text

SEA ICE DRIFT MONITORING IN THE BOHAI SEA BASED ON GF4 SATELLITE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-2419-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 2419-2425 ◽

Cited By ~ 1

Author(s):

Y. Zhao ◽

P. Wei ◽

H. Zhu ◽

B. Xing

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Satellite Images ◽

Bohai Sea ◽

Scale Invariant ◽

The Bohai Sea ◽

Invariant Feature ◽

Ice Drift ◽

Sea Ice Drift ◽

Scale Invariant Feature

The Bohai Sea is the inland sea with the highest latitude in China. In winter, the phenomenon of freezing occurs in the Bohai Sea due to frequent cold wave influx. According to historical records, there have been three serious ice packs in the Bohai Sea in the past 50 years which caused heavy losses to our economy. Therefore, it is of great significance to monitor the drift of sea ice and sea ice in the Bohai Sea. The GF4 image has the advantages of short imaging time and high spatial resolution. Based on the GF4 satellite images, the three methods of SIFT (Scale invariant feature &ndash; the transform and Scale invariant feature transform), MCC (maximum cross-correlation method) and sift combined with MCC are used to monitor sea ice drift and calculate the speed and direction of sea ice drift, the three calculation results are compared and analyzed by using expert interpretation and historical statistical data to carry out remote sensing monitoring of sea ice drift results. The experimental results show that the experimental results of the three methods are in accordance with expert interpretation and historical statistics. Therefore, the GF4 remote sensing satellite images have the ability to monitor sea ice drift and can be used for drift monitoring of sea ice in the Bohai Sea.

Download Full-text

The in situ observation of modelled sea ice drift characteristics in the Bohai Sea

Acta Oceanologica Sinica ◽

10.1007/s13131-019-1395-5 ◽

2019 ◽

Vol 38 (3) ◽

pp. 17-25 ◽

Cited By ~ 2

Author(s):

Yu Yan ◽

Wei Gu ◽

Yingjun Xu ◽

Qian Li

Keyword(s):

Sea Ice ◽

Bohai Sea ◽

In Situ Observation ◽

The Bohai Sea ◽

Ice Drift ◽

Sea Ice Drift

Download Full-text

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.

Download Full-text