Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5335-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-5335-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A comparison of constant false alarm rate object detection algorithms for iceberg identification in L- and C-band SAR imagery of the Labrador Sea
Laust Færch
CORRESPONDING AUTHOR
Center for Integrated Remote Sensing and Forecasting for Arctic Operations, Department of Physics and Technology, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
Wolfgang Dierking
Center for Integrated Remote Sensing and Forecasting for Arctic Operations, Department of Physics and Technology, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bussestr. 24, 27570 Bremerhaven, Germany
Nick Hughes
Norwegian Meteorological Institute, Kirkegårdsvejen, 60, 9293 Tromsø, Norway
Anthony P. Doulgeris
Center for Integrated Remote Sensing and Forecasting for Arctic Operations, Department of Physics and Technology, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
Related authors
No articles found.
Are Frode Kvanum, Cyril Palerme, Malte Müller, Jean Rabault, and Nick Hughes
The Cryosphere, 19, 4149–4166, https://doi.org/10.5194/tc-19-4149-2025, https://doi.org/10.5194/tc-19-4149-2025, 2025
Short summary
Short summary
Recent studies have shown that machine learning models are effective at predicting sea ice concentration, yet few have explored the development of such models in an operational context. In this study, we present the development of a machine learning forecasting system which can predict sea ice concentration at 1 km resolution up to 3 d ahead using real-time operational data. The developed forecasts predict the sea ice edge position with better accuracy than physical and baseline forecasts.
Jean Rabault, Trygve Halsne, Ana Carrasco, Anton Korosov, Joey Voermans, Patrik Bohlinger, Jens Boldingh Debernard, Malte Müller, Øyvind Breivik, Takehiko Nose, Gaute Hope, Fabrice Collard, Sylvain Herlédan, Tsubasa Kodaira, Nick Hughes, Qin Zhang, Kai Haakon Christensen, Alexander Babanin, Lars Willas Dreyer, Cyril Palerme, Lotfi Aouf, Konstantinos Christakos, Atle Jensen, Johannes Röhrs, Aleksey Marchenko, Graig Sutherland, Trygve Kvåle Løken, and Takuji Waseda
EGUsphere, https://doi.org/10.48550/arXiv.2401.07619, https://doi.org/10.48550/arXiv.2401.07619, 2024
Short summary
Short summary
We observe strongly modulated waves-in-ice significant wave height using buoys deployed East of Svalbard. We show that these observations likely cannot be explained by wave-current interaction or tide-induced modulation alone. We also demonstrate a strong correlation between the waves height modulation, and the rate of sea ice convergence. Therefore, our data suggest that the rate of sea ice convergence and divergence may modulate wave in ice energy dissipation.
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023, https://doi.org/10.5194/tc-17-5519-2023, 2023
Short summary
Short summary
To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
Short summary
Short summary
This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
Short summary
Short summary
Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Yu Zhang, Tingting Zhu, Gunnar Spreen, Christian Melsheimer, Marcus Huntemann, Nick Hughes, Shengkai Zhang, and Fei Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-85, https://doi.org/10.5194/tc-2021-85, 2021
Revised manuscript not accepted
Short summary
Short summary
We developed an algorithm for ice-water classification using Sentinel-1 data during melting seasons in the Fram Strait. The proposed algorithm has the OA of nearly 90 % with STD less than 10 %. The comparison of sea ice concentration demonstrate that it can provide detailed information of sea ice with the spatial resolution of 1km. The time series shows the average June to September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.
Cited articles
Akbari, V. and Brekke, C.: Iceberg Detection in Open and Ice-Infested Waters Using C-Band Polarimetric Synthetic Aperture Radar, IEEE T. Geosci. Remote, 56, 407–421, https://doi.org/10.1109/TGRS.2017.2748394, 2018.
Anfinsen, S. N., Doulgeris, A. P., and Eltoft, T.: Estimation of the Equivalent Number of Looks in Polarimetric Synthetic Aperture Radar Imagery, IEEE T. Geosci. Remote, 47, 3795–3809, https://doi.org/10.1109/TGRS.2009.2019269, 2009.
Argenti, F., Lapini, A., Bianchi, T., and Alparone, L.: A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images, IEEE Geosci. Remote Sens. Mag., 1, 6–35, https://doi.org/10.1109/MGRS.2013.2277512, 2013.
Bailey, J. and Marino, A.: Quad-Polarimetric Multi-Scale Analysis of Icebergs in ALOS-2 SAR Data: A Comparison between Icebergs in West and East Greenland, Remote Sensing, 12, 1864, https://doi.org/10.3390/rs12111864, 2020.
Bailey, J., Marino, A., and Akbari, V.: Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data, Remote Sensing, 13, 1753, https://doi.org/10.3390/rs13091753, 2021.
Barbat, M. M., Rackow, T., Hellmer, H. H., Wesche, C., and Mata, M. M.: Three Years of Near-Coastal Antarctic Iceberg Distribution From a Machine Learning Approach Applied to SAR Imagery, J. Geophys. Res.-Oceans, 124, 6658–6672, https://doi.org/10.1029/2019JC015205, 2019.
Bourbigot, M., Johnson, H., and Piantanda, R.: Sentinel-1 Product Definition, ESA, https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/document-library/-/asset_publisher/1dO7RF5fJMbd/content/sentinel-1-product-definition (last access: 6 December 2023), 2016.
Brekke, C.: Automatic ship detection based on satellite SAR, FFI, ISBN 978-82-464-1582-6, http://hdl.handle.net/20.500.12242/2139 (last access: 6 December 2023), 2009.
Brekke, C. and Anfinsen, S. N.: Ship Detection in Ice-Infested Waters Based on Dual-Polarization SAR Imagery, IEEE Geosci. Remote Sensing Lett., 8, 391–395, https://doi.org/10.1109/LGRS.2010.2078796, 2011.
Buus-Hinkler, J., Qvistgaard, K., and Krane, K. A. H.: Iceberg number density – Reaching a full picture of the Greenland waters, in: 2014 IEEE Geoscience and Remote Sensing Symposium, IGARSS 2014–2014 IEEE International Geoscience and Remote Sensing Symposium, Quebec City, QC, 270–273, https://doi.org/10.1109/IGARSS.2014.6946409, 2014.
Connetable, P., Conradsen, K., Nielsen, A. A., and Skriver, H.: Test Statistics for Reflection Symmetry: Applications to Quad-Polarimetric SAR Data for Detection of Man-Made Structures, IEEE J. Sel. Top. Appl., 15, 2877–2890, https://doi.org/10.1109/JSTARS.2022.3162670, 2022.
Conradsen, K., Nielsen, A. A., Schou, J., and Skriver, H.: A test statistic in the complex wishart distribution and its application to change detection in polarimetric SAR data, IEEE T. Geosci. Remote, 41, 4–19, https://doi.org/10.1109/TGRS.2002.808066, 2003.
Copernicus: Data Space Eco System, Copernicus [data set], https://dataspace.copernicus.eu/, last access: 6 December 2023.
Crisp, D. J.: The state-of-the-art in ship detection in synthetic aperture radar imagery, DSTO, Dept. Defense, Australian Government, Canberra, IC, Australia, 2004.
Danish Meteorological Institute: Verjarkiv, Danish Meteorological Institute [data set], https://www.dmi.dk/vejrarkiv/, last access: 14 September 2021.
Das, A., Kumar, R., and Rosen, P.: Nisar Mission Overview and Updates on ISRO Science Plan, in: 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), Ahmedabad, India, 269–272, https://doi.org/10.1109/InGARSS51564.2021.9791979, 2021.
Davidson, M., Gebert, N., and Giulicchi, L.: ROSE-L – The L-band SAR Mission for Copernicus, in: EUSAR 2021; 13th European Conference on Synthetic Aperture Radar, Leipzig, Germany 29 March 2021–1 April 2021, https://ieeexplore.ieee.org/servlet/opac?punumber=9472486 (last access: 9 December 2023), 2021.
Denbina, M. and Collins, M. J.: Iceberg Detection Using Compact Polarimetric Synthetic Aperture Radar, Atmos. Ocean, 50, 437–446, https://doi.org/10.1080/07055900.2012.733307, 2012.
Denbina, M. and Collins, M. J.: Iceberg Detection Using Simulated Dual-Polarized Radarsat Constellation Data, Can. J. Remote Sens., 40, 165–178, https://doi.org/10.1080/07038992.2014.945517, 2014.
Dierking, W.: Sea Ice And Icebergs. Maritime Surveillance with Synthetic Aperture Radar, edited by: Di Martino, G. and Iodice, A., Institution of Engineering and Technology, 346 pp., ISBN 9781785616013, https://doi.org/10.1049/SBRA521E, 2020.
Dierking, W. and Davidson, M.: Enhanced Sea Ice Monitoring At L- and C-Bands using Rose-L and Sentinel-1, in: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 4059–4060, https://doi.org/10.1109/IGARSS39084.2020.9323886, 2020.
Dierking, W. and Wesche, C.: C-Band Radar Polarimetry – Useful for Detection of Icebergs in Sea Ice?, IEEE T. Geosci. Remote, 52, 25–37, https://doi.org/10.1109/TGRS.2012.2234756, 2014.
Doulgeris, A. P., Anfinsen, S. N., and Eltoft, T.: Automated non-gaussian clustering of polarimetric synthetic aperture radar images, IEEE T. Geosci. Remote, 49, 3665–3676, https://doi.org/10.1109/TGRS.2011.2140120, 2011.
El-Darymli, K., McGuire, P., Power, D., and Moloney, C.: Target detection in synthetic aperture radar imagery: a state-of-the-art survey, J. Appl. Remote Sens, 7, 071598, https://doi.org/10.1117/1.JRS.7.071598, 2013.
Færch, L.: CFAR Object Detection Library (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.10254677, 2023.
Gill, R. S.: Operational Detection of Sea Ice Edges and Icebergs Using SAR, Can. J. Remote Sens., 27, 411–432, https://doi.org/10.1080/07038992.2001.10854884, 2001.
Gillies, S. et al.: Rasterio: geospatial raster I/O for Python programmers, GitHub [code], https://github.com/rasterio/rasterio (last access: 17 November 2022), 2013.
Goodman, N. R.: Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution (An Introduction), Ann. Math. Statist., 34, 152–177, https://doi.org/10.1214/aoms/1177704250, 1963.
Government of Canada: Environment and natural resources, Weather, Climate and Hazards, Past weather and climate, Historical Data, Government of Canada [data set], https://climate.weather.gc.ca, last access: 18 January 2023.
Gray, A. L. and Arsenault, L. D.: Time-delayed reflections in L-band synthetic aperture radar imagery of icebergs, IEEE T. Geosci. Remote, 29, 284–291, https://doi.org/10.1109/36.73670, 1991.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020.
Howell, C., Youden, J., Lane, K., Power, D., Randell, C., and Flett, D.: Iceberg and ship discrimination with ENVISAT multi-polarization ASAR, in: IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA, 2004, 113–116, https://doi.org/10.1109/IGARSS.2004.1368958, 2004.
JAXA: ALOS-2/PALSAR-2 Level 1.1/1.5/2.1/3.1 CEOS SAR Product Format Description, Rev. G, Japanese Aerospace Exploration Agency, https://www.eorc.jaxa.jp/ALOS/en/alos-2/datause/a2_format_e.htm (last access: 6 December 2023), 2012.
Karvonen, J., Gegiuc, A., Niskanen, T., Montonen, A., Buus-Hinkler, J., and Rinne, E.: Iceberg Detection in Dual-Polarized C-Band SAR Imagery by Segmentation and Nonparametric CFAR (SnP-CFAR), IEEE T. Geosci. Remote, 60, 4300812, https://doi.org/10.1109/TGRS.2021.3070312, 2022.
Kim, J.-W., Kim, D.-J., Kim, S.-H., and Hwang, B.-J.: Iceberg detection using full-polarimetric RADARSAT-2 SAR data in west antarctica, in: 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Seoul, Korea (South), 26–30 September 2011, 1–4, 2011.
Lam, S. K., Pitrou, A., and Seibert, S.: Numba: a LLVM-based Python JIT compiler, in: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis, Austin Texas, 1–6, https://doi.org/10.1145/2833157.2833162, 2015.
Lee, J.-S. and Pottier, E.: Polarimetric Radar Imaging: From Basics to Applications, 1st edn., edited by: Lee, J.-S. and Pottier, E., CRC Press, https://doi.org/10.1201/9781420054989, 2009.
Liu, C.: A dual-polarization ship detection algorithm, DRDC-RDDC-2015-R109, Ottawa Research Centre, 2015.
Liu, C.: Method for Fitting K-Distributed Probability Density Function to Ocean Pixels in Dual-Polarization SAR, Can. J. Remote Sens., 44, 299–310, https://doi.org/10.1080/07038992.2018.1491789, 2018.
Marino, A.: Iceberg Detection with L-Band ALOS-2 Data Using the Dual-POL Ratio Anomaly Detector, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, 6067–6070, https://doi.org/10.1109/IGARSS.2018.8519206, 2018.
Marino, A., Dierking, W., and Wesche, C.: A Depolarization Ratio Anomaly Detector to Identify Icebergs in Sea Ice Using Dual-Polarization SAR Images, IEEE T. Geosci. Remote, 54, 5602–5615, https://doi.org/10.1109/TGRS.2016.2569450, 2016.
Novak, L. M. and Hesse, S. R.: Optimal Polarizations for Radar Detection and Recognition of Targets in Clutter, The Record of the 1993 IEEE National Radar Conference, Lynnfield, MA, USA, 79–83, https://doi.org/10.1109/NRC.1993.270489, 1993.
Oliver, C. and Quegan, S.: Understanding synthetic aperture radar images, SciTech Publishing Inc. Raleigh, NC 27613, ISBN 1-891121-31-6, 2004.
OpenStreetMap contributors: Land Polygons, https://osmdata.openstreetmap.de/data/land-polygons.html, last access: 18 January 2023, 2015.
Park, J.-W., Won, J.-S., Korosov, A. A., Babiker, M., and Miranda, N.: Textural Noise Correction for Sentinel-1 TOPSAR Cross-Polarization Channel Images, IEEE T. Geosci. Remote, 57, 4040–4049, https://doi.org/10.1109/TGRS.2018.2889381, 2019.
Power, D., Youden, J., Lane, K., Randell, C., and Flett, D.: Iceberg Detection Capabilities of RADARSAT Synthetic Aperture Radar, Can. J. Remote Sens., 27, 476–486, https://doi.org/10.1080/07038992.2001.10854888, 2001.
Salkind, N. J.: Bonferroni test, in: Encyclopedia of Measurement and Statistics, Vol. 0, Sage Publications, Inc., 104–107, https://doi.org/10.4135/9781412952644, 2007.
Sandven, S., Babiker, M., and Kloster, K.: Iceberg observations in the Barents Sea by radar and optical satellite images, in: Proceedings of the ENVISAT Symposium, Montreux, Switzerland, 23–27 April 2007, ISBN 92-9291-200-1, 2007. 2007.
Schou, J., Skriver, H., Nielsen, A. A., and Conradsen, K.: CFAR edge detector for polarimetric SAR images, IEEE T. Geosci. Remote, 41, 20–32, https://doi.org/10.1109/TGRS.2002.808063, 2003.
Soldal, I., Dierking, W., Korosov, A., and Marino, A.: Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images, Remote Sensing, 11, 806, https://doi.org/10.3390/rs11070806, 2019.
Tao, D., Doulgeris, A. P., and Brekke, C.: A Segmentation-Based CFAR Detection Algorithm Using Truncated Statistics, IEEE T. Geosci. Remote, 54, 2887–2898, https://doi.org/10.1109/TGRS.2015.2506822, 2016a.
Tao, D., Anfinsen, S. N., and Brekke, C.: Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations, IEEE T. Geosci. Remote, 54, 117–134, https://doi.org/10.1109/TGRS.2015.2451311, 2016b.
Tunaley, J. K. E.: K-Distribution Algorithm, LRDC Technical Report, August, 2010.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
Wesche, C. and Dierking, W.: Iceberg signatures and detection in SAR images in two test regions of the Weddell Sea, Antarctica, J. Glaciol., 58, 325–339, https://doi.org/10.3189/2012J0G11J020, 2012.
Wesche, C. and Dierking, W.: Estimating iceberg paths using a wind-driven drift model, Cold Reg. Sci. Technol., 125, 31–39, https://doi.org/10.1016/j.coldregions.2016.01.008, 2016.
Willis, C. J., Macklin, J. T., Partington, K. C., Teleki, K. A., Rees, W. G., and Williams, R. G.: Iceberg detection using ERS-1 Synthetic Aperture Radar, Int. J. Remote Sens., 17, 1777–1795, https://doi.org/10.1080/01431169608948739, 1996.
Yang, W., Li, Y., Liu, W., Chen, J., Li, C., and Men, Z.: Scalloping Suppression for ScanSAR Images Based on Modified Kalman Filter With Preprocessing, IEEE T. Geosci. Remote, 59, 7535–7546, https://doi.org/10.1109/TGRS.2020.3034098, 2021.
Zakharov, I., Power, D., Howell, M., and Warren, S.: Improved detection of icebergs in sea ice with RADARSAT-2 polarimetric data, in: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 2017, 2294–2297, https://doi.org/10.1109/IGARSS.2017.8127448, 2017.
Short summary
Icebergs in open water are a risk to maritime traffic. We have compared six different constant false alarm rate (CFAR) detectors on overlapping C- and L-band synthetic aperture radar (SAR) images for the detection of icebergs in open water, with a Sentinel-2 image used for validation. The results revealed that L-band gives a slight advantage over C-band, depending on which detector is used. Additionally, the accuracy of all detectors decreased rapidly as the iceberg size decreased.
Icebergs in open water are a risk to maritime traffic. We have compared six different constant...