Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 5, pp. 23-34

Digital processing of Sentinel-1 data for automated detection of old ice edge

N.Yu.‎ Zakhvatkina‎ 1, 2 , I.A. Bychkova 1 , V.G. Smirnov 1 
1 Arctic and Antarctic Research Institute, Saint Petersburg, Russia
2 Nansen International Environmental Remote Sensing Center, Saint Petersburg, Russia
Accepted: 29.07.2020
DOI: 10.21046/2070-7401-2020-17-5-23-34
An automated technique for old ice edge detection based on the neural networks (NN) method is described. NN classification algorithm is based on Sentinel-1 extra wide mode dual polarized synthetic aperture radar (SAR) imagery acquired over the Arctic under winter conditions. This SAR data has several features and an approach to improve data quality has been proposed. NN training was conducted using a backpropagation algorithm. Since various sea ice types can have the same backscattering coefficients, the texture features have been used as accompanying data. Selection of the most informative texture features is justified. The optimal NN topology was found based on the analysis of classification errors and processing time. The ice charts provided by the Arctic and Antarctic Research Institute (AARI) and visual ice expert’s estimation were used for verification of the NN classification results. It is shown that SAR data can be used for automatic identification of several sea ice stages of development and old ice boundary mapping. The error matrices were calculated with the classification accuracies for each class using AARI ice charts as a source of reference data. The classification accuracy of the first-year and old ice was 75 and 90 % respectively.
Keywords: sea ice, old ice edge, Arctic, Sentinel-1, synthetic aperture radar, classification, texture, neural networks
Full text

References:

  1. Metodicheskie ukazaniya po voprosam priema, obrabotki i ispol’zovaniya sputnikovoi ledovoi informatsii. Vypusk 1: Opredelenie kharakteristik ledyanogo pokrova po radiolokatsionnym snimkam ISZ “Kosmos-1500” (Guidelines for reception, processing and use of satellite ice information, Issue 1: Determination of the ice cover characteristics from Cosmos-1500 satellite radar images), Leningrad: AARI, 1985, 75 p.
  2. Smirnov V. G., Bushuev A. V., Zakhvatkina N. Yu., Loshchilov V. S., Sputnikovyi monitoring morskikh l’dov (Satellite Monitoring of the Sea Ice), Problemy Arktiki i Antarktiki, 2010, Vol. 85, No. 2, pp. 62–76.
  3. Haykin S., Neural Networks: A Comprehensive Foundation, Second Edition, Moscow: Williams, 2006, 1104 p.
  4. Albregtsen F., Statistical Texture Measures Computed from Gray Level Coocurrence Matrices, Image Processing Laboratory Department of Informatics, University of Oslo, 2008, pp. 1–14.
  5. Clausi D. A., An analysis of co-occurrence texture statistics as a function of grey level quantization, Canadian J. Remote Sensing, 2002, Vol. 28, pp. 45–62.
  6. Dierking W., Sea ice monitoring by synthetic aperture radar, Oceanography, 2013, Vol. 26, pp. 100–111.
  7. Haralick R. M., Shanmugam K. S., Dinstein I., Textural features for image classification, IEEE Trans. Systems, Man, and Cybernetics, 1973, Vol. 3, pp. 610–621.
  8. Komarov A. S., Buehner M., Detection of First-Year and Multi-Year Sea Ice from Dual-Polarization SAR Images Under Cold Conditions, IEEE Trans. Geoscience and Remote Sensing, 2019, Vol. 57, No. 11, pp. 9109–9123.
  9. Kwok R., The Radarsat Geophysical Processor System, In: Analysis of SAR Data of the Polar Oceans, Berlin-Heidelberg: Springer, 1998, pp. 235–257.
  10. Leigh S., Zhijie W., Clausi D. A., Automated Ice–Water Classification Using Dual Polarization SAR Satellite Imagery, IEEE Trans. Geoscience and Remote Sensing, 2014, Vol. 52, pp. 5529–5539.
  11. Mäkynen M., Karvonen J., Incidence Angle Dependence of First-Year Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery Over the Kara Sea, IEEE Trans. Geoscience and Remote Sensing, 2017, Vol. 55, No. 11, pp. 6170–6181.
  12. Sentinel-1 Level 1 Detailed Algorithm Definition, Technical Document, Issue 2/2, ESA, 2019, 158 p.
  13. Shokr M. E., Evaluation of Second-Order Texture Parameters for Sea Ice Classification from Radar Images, J. Geophysical Research, 1991, Vol. 96, pp. 10625–10640.
  14. Soh L. K., Tsatsoulis C., ARCTOS: A Knowledge Engineering Software Tool for Images, Intern. J. Human-Computer Studies, 2002, Vol. 57, No. 6, pp. 469–496.
  15. Thermal denoising of products generated by the Sentinel-1 IPF, Technical Document, Issue 1.1, ESA, 2017, 25 p.
  16. Zakhvatkina N., Korosov A., Muckenhuber S., Sandven S., Babiker M., Operational algorithm for ice water classification on dual-polarized Radarsat-2 images, The Cryosphere, 2017, Vol. 11, No. 1, pp. 33–46.
  17. Zakhvatkina N., Smirnov V., Bychkova I., Satellite SAR Data-based Sea Ice Classification: An Overview, Geosciences, 2019, Vol. 9(4), pp. 152, DOI: 10.3390/geosciences9040152.