Using the Neural Network Technique for Lead Detection in Radar Images of Arctic Sea Ice

IF 1.4 4区地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES

Russian Meteorology and Hydrology Pub Date : 2024-06-27 DOI:10.3103/s1068373924040083

N. Yu. Zakhvatkina, I. A. Bychkova, V. G. Smirnov

引用次数: 0

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.

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利用神经网络技术检测北极海冰雷达图像中的铅含量

摘要本文介绍了一种利用 "哨兵-1 号 "卫星在超宽扫描模式下提供的双偏振合成孔径雷达（SAR）数据从海冰中区分线索的算法。该算法利用合成孔径雷达图像中获得的海面信号的极化特征：共极化和交叉极化之间的比率。提出了一种对合成孔径雷达图像进行分类的技术，利用极化比和极化差以及纹理特征和神经网络实现来识别漂移海冰中的不连续性（裂缝、引线）。利用在俄罗斯联邦北极海域获得的卫星数据对该方法进行了测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Russian Meteorology and Hydrology METEOROLOGY & ATMOSPHERIC SCIENCES-

CiteScore

1.70

自引率

28.60%

发文量

审稿时长

4-8 weeks

期刊介绍： Russian Meteorology and Hydrology is a peer reviewed journal that covers topical issues of hydrometeorological science and practice: methods of forecasting weather and hydrological phenomena, climate monitoring issues, environmental pollution, space hydrometeorology, agrometeorology.