ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


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
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