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一种改进的星载GNSS-R卷积神经网络海冰检测方法

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卷积神经网络(convolutional neural network,CNN)已用于星载全球导航卫星系统反射测量(global navigation satellite system-reflectometry,GNSS-R)海冰检测,其具有数据预处理简单、最大限度保留反射面信息等优势,但已有GNSS-R CNN海冰检测方法研究的数据集时间跨度较小,代表性有限,且未考虑训练集内海水、海冰时延多普勒图(delay-Doppler map,DDM)的比例对方法泛化能力的影响.针对该问题,首先提出一种筛除畸形DDM方法,有效筛除错误数据;然后,设计合适的CNN结构及参数,通过小样本对比实验发现CNN模型在训练集内海水、海冰DDM的比例为1∶1时具有高准确率和最佳泛化能力,并优化数据集选取策略;最后使用2018年全年大样本数据集评估改进的方法在大数据量和大时间跨度时的有效性和可靠性.研究表明,改进的方法通过加强数据质量控制、优化数据集选取策略,提升了 CNN海冰检测方法的泛化能力及可靠性,使其更适用于实际应用场景,为海冰消融等研究提供参考.
An Improved Sea Ice Detection Method Based on Spaceborne GNSS-R Using CNN
Objectives:Convolutional neural network(CNN)has been used in spaceborne global navigation satellite system-reflectometry(GNSS-R)sea ice detection,which has the advantages of simple data pre-processing and maximum retention of reflector information.However,the data sets used in previous studies of the GNSS-R CNN sea ice detection method have a small span in time and limited representativeness,and the influence of the delay-Doppler map(DDM)ratio of seawater and sea ice in the training set on the generalization ability of the method is not considered.Methods:To solve these problems,a method of screening out malformed DDM is proposed.The appropriate CNN structure and parameters are designed,and the dataset selection strategy is optimized through comparative tests of small samples.A large sample dataset from 2018 is used to evaluate the validity and reliability of the improved method in the case of large data volume and large time.Results and Conclusions:The results show that the proposed method can screen the wrong data effectively.The CNN model has high accuracy and the best generalization ability when the DDM ratio of seawater and sea ice in the training set is 1∶1,and the improved method is still effective and reliable in large data volume and large time span.The improved method improves the generaliza-tion ability and reliability of the CNN sea ice detection method by strengthening data quality control and op-timizing the dataset selection strategy,to make it more applicable to practical application scenarios and pro-vide a reference for studies on sea ice melting.

convolutional neural network(CNN)delay-Doppler map(DDM)sea ice detectionglobal navigation satellite system-reflectometry(GNSS-R)

叶世榕、罗歆琪、南阳、夏朋飞

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武汉大学卫星导航定位技术研究中心,湖北 武汉,430079

天津大学海洋科学与技术学院,天津,300072

卷积神经网络 时延多普勒图 海冰检测 全球导航卫星系统反射测量技术

国家自然科学基金武汉大学测绘遥感信息工程国家重点实验室专项科研项目国家重点研发计划中交公规院科技创新专项

419740312019YFC1509603C22030A

2024

10.13203/j.whugis20220585

武汉大学学报(信息科学版)
武汉大学

武汉大学学报(信息科学版)

CSTPCD北大核心
影响因子：1.072
ISSN：1671-8860
年,卷(期)：2024.49(1)
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