基于特征匹配的采用Sentinel-1影像提取海冰漂移矢量算法研究
Sea ice drift vectors extraction based on feature tracking to Sentinel-1 images
- 2022年 页码：1-10
网络出版日期： 2022-09-20
DOI： 10.11834/jrs.20222238
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摘要
海冰漂移对北极气候研究和人类活动保障有重要意义。对星载辐射计或散射计数据采用模板匹配法提取的海冰漂移矢量结果存在空间分辨率较低及精度不佳等问题。合成孔径雷达（SAR）影像具有更高的空间分辨率，结合特征匹配算法可用于提取高分辨率海冰漂移场。本文采用Sentinel-1影像为数据源，比较了四种常用的特征匹配算子SIFT、SURF、ORB、A-AKZE提取北极海冰漂移矢量的效果，并分析了HH与HV极化通道提取的漂移矢量提取的空间分布差异。针对特征匹配中不可避免的错误，结合既有算法的优势本研究提出了一套高效且准确的错误矢量滤除算法。本研究利用MOSAiC浮标定位数据验证了该方法提取的海冰漂移矢量的精度，并与基于Sentinel-1影像的既有海冰漂移产品进行了精度对比。实验结果表明基于A-KAZE算子的算法在提取结果的数量与分布上优于SIFT、SURF与ORB算子。HH与HV极化通道影像提取的海冰漂移矢量在数量与空间分布上有所差异，结合两者可有效扩大海冰监测范围。本文采用的错误矢量滤除算法能高效滤除错误匹配的同时保留更多正确漂移矢量。基于A-KAZE算子提取的海冰漂移矢量平均速度误差低于0.2 km/d，平均方向误差低于1°，与同样基于Sentinel-1 SAR影像但采用模板匹配法的DTU海冰漂移产品具有较高的一致性，但本方法提取的海冰漂移矢量具有更高的空间分辨率及更大的覆盖范围。
Abstract
Sea ice drift is an important natural phenomenon in the Arctic
which is of great significance for climate research and human activities such as shipping security in the Arctic area. At present
sea ice drift products are often derived with space-borne radiometer and scatterometer with template matching algorithm and suffered from low resolution and low accuracy. Sentinel-1 synthetic aperture radar imagery advantages of high spatial resolution
and holds great potential for deriving sea ice drift fields with high resolution and high accuracy by applying feature matching algorithms.Using two pairs of Sentinel-1 Arctic sea ice SAR images
this research compared sea ice drift results derived from four popular features including SIFT
SURF
ORB and A-KAZE
and analyzed the similarities and differences between HH and HV imagery in terms of spatial distribution and coverage of the derived sea ice drift vectors. Aiming at identifying wrong vectors after the NNDR test with high calculation efficiency and accuracy
we proposed a filtering method combined with two published methods. At last
we evaluated the sea ice drift vectors' accuracy by comparing our derived results and DTU sea ice products to GPS data of MOSAiC buoys.Employing A-KAZE features to Sentinel-1 EW imagery can effectively derive the sea ice drift fields with high spatial resolution and coverage rates. A-KAZE feature performs better than SIFT
SURF and ORB in terms of spatial distribution and the number of vectors. Combining the vectors obtained from HH and HV polarization imagery can effectively extend the coverage of sea ice motion fields. The wrong vector filter checks the similarity of a vector to its neighbours only if its speed or direction exceeds 2 times the standard deviation. It improves computational efficiency and retains more correct vectors compared to two traditional methods. Validation with MOSAiC buoys data found that the average speed error of sea ice drift vectors extracted with the proposed A-KAZE based method was less than 0.2 km/day
and the average direction error was less than 1 °
which shared a high consistency with DTU sea ice drift products which also employs Sentinel-1 SAR imagery but applying the template matching algorithm
but our proposed methods presented a higher spatial coverage.This study demonstrates the potential of deriving sea ice drift vectors by applying dual-polarized Sentinel-1 SAR imagery and A-KAZE features. It can effectively and quickly generate high spatial resolution sea ice drift vector fields with high spatial covering rates and high accuracy
which can serve as an accurate data source for climate research and maritime security in the Arctic.
关键词
北极海冰漂移矢量图像特征匹配A-KAZESentinel-1
Keywords
ArcticSea ice driftImage feature trackingA-KAZESentinel-1
references
Alcantarilla P F, Nuevo J, Bartoli A. 2013. Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Proceedings of the British Machine Vision Conference, [DOI 10.5244/C.27.13http://dx.doi.org/10.5244/C.27.13]
Bay H, Ess A, Tuytelaars T, Van L. 2008. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110: 346-359. [DOI 10.1016/j.cviu.2007.09.014http://dx.doi.org/10.1016/j.cviu.2007.09.014]
Demchev D, Volkov V, Kazakov E, Alcantarilla P. F, Sandven S, Khmeleva. V. 2017. Sea Ice Drift Tracking From Sequential SAR Images Using Accelerated-KAZE Features. IEEE Transactions on Geoscience and Remote Sensing, 55: 5174-5184. [DOI 10.1109/Tgrs.2017.2703084http://dx.doi.org/10.1109/Tgrs.2017.2703084]
Dybkjaer G 2018. Medium Resolution Sea Ice Drift Product User Manual;Version 2.0;. The Ocean and Sea Ice Satellite Application Facility(OSI SAF). Available online: https://osisaf-hl.met.no/sites/osisaf-hl/files/user_manuals/osisaf_ss2_pum_sea-ice-driftmr_v2p0.pdf (accessed on 17 April 2022)https://osisaf-hl.met.no/sites/osisaf-hl/files/user_manuals/osisaf_ss2_pum_sea-ice-driftmr_v2p0.pdf(accessedon17April2022).
Emery W, Fowler C, Hawkins J, Preller RH. 1991. Fram Strait Satellite Image-Derived Ice Motions. Journal of Geophysical Research Oceans, [DOI 10.1029/90JC02273http://dx.doi.org/10.1029/90JC02273]
Ezraty R, Girard-ardhuin F, Piollé J.F, Kaleschke L, Heygster G. 2007a. Arctic and Antarctic Sea-Ice Concentration and Arctic Sea Ice Drift Estimated from Special Sensor Microwave Imager Data. User’s Manual, Version 2.1, French Research Institute for the Exploitation of the Seas (Ifremer). Available online: ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psidrift/documentation/ssmi.pdf (accessed on 17 April 2022).
Ezraty R, Girard-ardhuin F, Piollé J F. Sea-Ice Drift in the Central Arctic Estimated from SeaWinds/QuikSCAT Backscatter Maps.User’s Manual, Version 2.2, French Research Institute for the Exploitation of the Seas (Ifremer), February 2007b. Available online:ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/psi-drift/documentation/quikscat.pdf (accessed on 17 April 2022).
Grosfeld K, Treffeisen R, Asseng J B, Bartsch A. 2016. Online sea-ice knowledge and data platform <www.meereisportal.de>. Polarforschung, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research & German Society of Polar Research, 85 (2), 143-155,.[DOI 10.2312]
Heil P, Fowler C, Maslanik J, Emery WJ, Allison I. 2001. A comparison of East Antarctic sea-ice motion derived using drifting buoys and remote sensing. Annals of Glaciology.[DOI 10.3189/172756401781818374http://dx.doi.org/10.3189/172756401781818374]
Karvonen J, Similä M, Lehtiranta J. 2007. SAR-based estimation of the baltic sea ice motion.IEEE International Geoscience & Remote Sensing Symposium, 2605-2608, [DOI 10.1109/Igarss.2007.4423378http://dx.doi.org/10.1109/Igarss.2007.4423378]
Komarov A, Barber D. 2012. Detection of Sea Ice Motion from Co- and Cross-Polarization Radarsat-2 Images. 2012 Ieee International Geoscience and Remote Sensing Symposium (Igarss), 3277-3280, [DOI 10.1109/Igarss.2012.6350604http://dx.doi.org/10.1109/Igarss.2012.6350604]
Korosov A, Rampal P. 2017. A Combination of Feature Tracking and Pattern Matching with Optimal Parametrization for Sea Ice Drift Retrieval from SAR Data. Remote Sensing, 9, [DOI 10.3390/rs9030258http://dx.doi.org/10.3390/rs9030258]
Lavergne T. 2016. Low Resolution Sea Ice Drift. Product User’s Manual; Version 1.8; The Ocean and Sea Ice Satellite Application Facility(OSI SAF). Available online: https://osisaf-hl.met.no/sites/osisaf-hl.met.no/files/user_manuals/osisaf_cdop2_ss2_pum_sea-ice-drift-lr_v1p8.pdf (accessed on 17 April 2022)https://osisaf-hl.met.no/sites/osisaf-hl.met.no/files/user_manuals/osisaf_cdop2_ss2_pum_sea-ice-drift-lr_v1p8.pdf(accessedon17April2022).
Liu A K, Zhao Y H, Wu S Y. 1999. Arctic sea ice drift from wavelet analysis of NSCAT and special sensor microwave imager data. Journal of Geophysical Research-Oceans, 104: 11529-11538.[Doi 10.1029/1998jc900115http://dx.doi.org/10.1029/1998jc900115]
Lowe D G 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60: 91-110. [DOI 10.1023/B:Visi.0000029664.99615.94http://dx.doi.org/10.1023/B:Visi.0000029664.99615.94]
Muckenhuber S, Korosov A A, Sandven S. 2016. Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery. Cryosphere, 10: 913-925. [DOI 10.5194/tc-10-913-2016http://dx.doi.org/10.5194/tc-10-913-2016]
Muja M, Lowe D G 2009. Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. Visapp 2009: Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, Vol 1: 331-340. <Go to ISI>://WOS:000266585100053
Ninnis R M, Emery W J, COLLINS M J. 1986. Automated extraction of pack ice motion from advanced very high resolution radiometer imagery. JOURNAL OF GEOPHYSICAL RESEARCH. [DOI 10.1029/JC091iC09p10725http://dx.doi.org/10.1029/JC091iC09p10725]
Rublee E, Rabaud V, Konolige K, Bradski G. 2011. ORB: an efficient alternative to SIFT or SURF. 2011 Ieee International Conference on Computer Vision (Iccv). 2564-2571. [DOI 10.1109/iccv.2011.6126544http://dx.doi.org/10.1109/iccv.2011.6126544]
Saldo R, Hackett B. 2020. Product User Manual for Global High Resolution SAR Sea Ice Drift SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006; Issue 2.12, Space Department/Institute at the Technical University of Denmark (DTU Space). Available online:https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-SI-PUM-011-006.pdf (accessed on 17 April 2022)https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-SI-PUM-011-006.pdf(accessedon17April2022).
Stroeve J C, Serreze M C, Holland M M, Kay J E, Malanik J, Barrett A P. 2012. The Arctic's rapidly shrinking sea ice cover: a research synthesis. Climatic Change, 110: 1005-1027. [DOI 10.1007/s10584-011-0101-1http://dx.doi.org/10.1007/s10584-011-0101-1]
Thomas M, Geiger C A, Kambhamettu C 2008. High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. Cold Regions Science and Technology, 52: 207-223. [DOI 10.1016/j.coldregions.2007.06.006http://dx.doi.org/10.1016/j.coldregions.2007.06.006]
Thomas M, Kambhamettu C, Geiger C A. 2011. Motion Tracking of Discontinuous Sea Ice. IEEE Transactions on Geoscience and Remote Sensing, 49: 5064-5079. [DOI 10.1109/Tgrs.2011.2158005http://dx.doi.org/10.1109/Tgrs.2011.2158005]
Tschudi M, Meier W N, Stewart J S, Fowler C, Maslanik J. 2019. Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors;Version 4. [The Arctic Region]; NASA National Snow and Ice Data Center, Available online: https://doi.org/10.5067/INAWUWO7QH7B (accessed on 17 April 2022)https://doi.org/10.5067/INAWUWO7QH7B(accessedon17April2022).
Wang X, Chen R T, Li C, Chen Z Q, Hui F M, Cheng X. 2022. An Intercomparison of Satellite Derived Arctic Sea Ice Motion Products. Remote Sensing, 14. [DOI 10.3390/rs14051261http://dx.doi.org/10.3390/rs14051261]
Wang J K. 2019. Research on Sea Ice Drift Detection Technology
Based on Combination of Feature Tracking and Pattern Matching.
王军凯 2019. 基于特征跟踪和模式匹配结合算法的海冰漂移检测技术研究. 内蒙古科技大学. [DOI 10.27724/d.cnki.gnmgk.2019.000187http://dx.doi.org/10.27724/d.cnki.gnmgk.2019.000187]
Zhang M, Wang J K, Lv X Q. 2019. Detection of Sea Ice Drift Based on Different Polarization Data Laser and Optoelectronics Progress, 56: 138-143. (张明, 王军凯, 吕晓琪. 2019. 结合不同极化数据的海冰漂移检测. 激光与光电子学进展. 56: 138-143.) DOI 10.3788/LOP56.101008http://dx.doi.org/10.3788/LOP56.101008
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