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

Authors :
S. Muckenhuber
A. A. Korosov
S. Sandven
Source :
The Cryosphere, Vol 10, Iss 2, Pp 913-925 (2016)
Publication Year :
2016
Publisher :
Copernicus Publications, 2016.

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.

Details

Language :
English
ISSN :
19940416 and 19940424
Volume :
10
Issue :
2
Database :
Directory of Open Access Journals
Journal :
The Cryosphere
Publication Type :
Academic Journal
Accession number :
edsdoj.5cb769eb00b342a69f32aade8655a51c
Document Type :
article
Full Text :
https://doi.org/10.5194/tc-10-913-2016