Abstract
The paper describes an algorithm to differentiate leads from sea ice using the dual polarization synthetic aperture radar (SAR) data from the Sentinel-1 satellite in an extrawide swath mode. The algorithm uses the polarimetric features of the sea surface signal obtained in the SAR images: the ratio between co- and cross-polarization. A technique is proposed for classifying the SAR images to identify discontinuities (cracks, leads) in drifting sea ice using the ratio and difference of polarizations together with texture features and the neural network implementation. The method was tested using the satellite data obtained over the Arctic seas in the Russian Federation.
This is a preview of subscription content, log in via an institution to check access.
REFERENCES
L. N. Dyment, P. V. Aksenov, S. M. Losev, and V. S. Porubaev, "Influence of the Spatial Resolution of Satellite Images on the Values of the Characteristics of Ice-cover Leads in the Arctic Seas," Issledovanie Zemli iz Kosmosa, No. 3 (2021) [Izv., Atmos. Oceanic Phys., 57 (2021)].
N. Yu. Zakhvatkina, I. A. Bychkova, and V. G. Smirnov, "Digital Processing of Sentinel-1 Data for Automated Detection of Old Ice Edge," Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, No. 5, 17 (2020).
S. M. Losev, L. N. Dyment, and E. U. Mironov, "Length of Large Leads in Drifting Ice of the Atlantic Sector of the Arctic Basin according to Satellite Imagesry," Led i Sneg, No. 4, 57 (2017).
Satellite Methods for Determination of Sea Ice Characteristics, Ed. by V. G. Smirnov (AANII, St. Petersburg, 2011) [in Russian].
W. Dierking, "Sea Ice Monitoring by Synthetic Aperture Radar," Oceanography, 26 (2013).
Y. Han, Y. Gao, Y. Zhang, J. Wang, and S. Yang, "Hyperspectral Sea Ice Image Classification Based on the Spectral-spatial-joint Feature with Deep Learning," Remote Sens., 11 (2019).
R. M. Haralick, K. S. Shanmugam, and I. Dinstein, "Textural Features for Image Classification," IEEE Trans. Syst. Man. Cyb., 3 (1973).
J. P. Hoffman, S. A. Ackerman, Y. Liu, and J. R. Ke, "The Detection and Characterization of Arctic Sea Ice Leads with Satellite Imagers," Remote Sens., 11 (2019).
R. Huang, C. Wang, J. Li, and Y. Sui, "DF-UHRNet: A Modified CNN-based Deep Learning Method for Automatic Sea Ice Classification from Sentinel-1A/B SAR Images," Remote Sens., 15 (2023).
N. Ivanova, P. Rampal, and S. Bouillon, "Error Assessment of Satellite-derived Lead Fraction in the Arctic," The Cryosphere, 10 (2016).
S. Khaleghian, H. Ullah, T. Kramer, N. Hughes, T. Eltoft, and A. Marinoni, "Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks," Remote Sens., 13 (2021).
J. Lohse, A. P. Doulgeris, and W. Dierking, "Map** Sea-ice Types from Sentinel-1 Considering the Surface-type Dependent Effect of Incidence Angle," Ann. Glaciol., 61 (2020).
N. Longepe, P. Thibaut, R. Vadaine, J.-C. Poisson, A. Guillot, F. Boy, and F. Borde, "Comparative Evaluation of Sea Ice Lead Detection Based on SAR Imagery and Altimeter Data," IEEE Trans. Geosci. Remote Sens., 57 (2019).
D. Murashkin, G. Spreen, M. Huntemann, and W. Dierking, "Method for Detection of Leads from Sentinel-1 SAR Images," Ann. Glaciol., 59 (2018).
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated from Meteorologiya i Gidrologiya, 2024, No. 4, pp. 91-103. https://doi.org/10.52002/0130-2906-2024-4-91-103.
Publisher’s Note. Allerton Press remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Zakhvatkina, N.Y., Bychkova, I.A. & Smirnov, V.G. Using the Neural Network Technique for Lead Detection in Radar Images of Arctic Sea Ice. Russ. Meteorol. Hydrol. 49, 346–353 (2024). https://doi.org/10.3103/S1068373924040083
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S1068373924040083