Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery

被引:27
作者
Aldenhoff, Wiebke [1 ]
Heuze, Celine [2 ]
Eriksson, Leif E. B. [1 ]
机构
[1] Chalmers Univ Technol, Dept Space Earth & Environm, Gothenburg, Sweden
[2] Univ Gothenburg, Dept Marine Sci, Gothenburg, Sweden
关键词
ice and climate; polar and subpolar oceans; sea ice; ARCTIC SEA-ICE; WATER CLASSIFICATION; X-BAND; ALGORITHM; VARIABILITY; TRENDS; MOTION; OCEAN; HEAT;
D O I
10.1017/aog.2018.7
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
In this paper an algorithm for ice/water classification of C-and L-band dual polarization synthetic aperture radar data is presented. A comparison of the two different frequencies is made in order to investigate the potential to improve classification results with multi-frequency data. The algorithm is based on backscatter intensities in co-and cross-polarization and autocorrelation as a texture feature. The mapping between image features and ice/water classification is made with a neural network. Accurate ice/water maps for both frequencies are produced by the algorithm and the results of two frequencies generally agree very well. Differences are found in the marginal ice zone, where the time difference between acquisitions causes motion of the ice pack. C-band reliably reproduces the outline of the ice edge, while L-band has its strengths for thin ice/calm water areas within the icepack. The classification shows good agreement with ice/water maps derived from met. no ice-charts and radiometer data from AMSR-2. Variations are found in the marginal ice zone where the generalization of the ice charts and lower accuracy of ice concentration from radiometer data introduce deviations. Usage of high resolution dual frequency data could be beneficial for improving ice cover information for navigation and modelling.
引用
收藏
页码:112 / 123
页数:12
相关论文
共 50 条
[41]   Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years [J].
Smedsrud, Lars H. ;
Halvorsen, Mari H. ;
Stroeve, Julienne C. ;
Zhang, Rong ;
Kloster, Kjell .
CRYOSPHERE, 2017, 11 (01) :65-79
[42]   Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices [J].
Soh, LK ;
Tsatsoulis, C .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1999, 37 (02) :780-795
[43]   Sea ice remote sensing using AMSR-E 89-GHz channels [J].
Spreen, G. ;
Kaleschke, L. ;
Heygster, G. .
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2008, 113 (C2)
[44]   Selecting and interpreting measures of thematic classification accuracy [J].
Stehman, SV .
REMOTE SENSING OF ENVIRONMENT, 1997, 62 (01) :77-89
[45]   Effects of Arctic Sea Ice Decline on Weather and Climate: A Review [J].
Vihma, Timo .
SURVEYS IN GEOPHYSICS, 2014, 35 (05) :1175-1214
[46]   Sea Ice Detection in the Sea of Okhotsk Using PALSAR and MODIS Data [J].
Wakabayashi, Hiroyuki ;
Mori, Yuta ;
Nakamura, Kazuki .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, 6 (03) :1516-1523
[47]   Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network [J].
Wang, Lei ;
Scott, K. Andrea ;
Clausi, David A. .
REMOTE SENSING, 2017, 9 (05)
[48]   Comparison of the ASI Ice Concentration Algorithm With Landsat-7 ETM+ and SAR Imagery [J].
Wiebe, Heidrun ;
Heygster, Georg ;
Markus, Thorsten .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2009, 47 (09) :3008-3015
[49]   Operational algorithm for ice-water classification on dual-polarized RADARSAT-2 images [J].
Zakhvatkina, Natalia ;
Korosov, Anton ;
Muckenhuber, Stefan ;
Sandven, Stein ;
Babiker, Mohamed .
CRYOSPHERE, 2017, 11 (01) :33-46
[50]   Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images [J].
Zakhvatkina, Natalia Yu ;
Alexandrov, Vitaly Yu ;
Johannessen, Ola M. ;
Sandven, Stein ;
Frolov, Ivan Ye .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (05) :2587-2600