首页|基于底质分类的浅海海域遥感水深反演

基于底质分类的浅海海域遥感水深反演

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近年来,卫星遥感影像水深反演一直是国内外研究热点,以往的遥感影像水深反演模型多基于底质均一的条件,缺乏对混合海底底质的研究.针对此问题,本文提出基于底质分类视角的遥感影像水深反演模型.以中国海南岛周边的蜈支洲岛与附近卫星影像为试验数据,对其进行预处理与底质分类后,分别使用双向长短期记忆网络(Bi-LSTM)模型、Stumpf模型与一维卷积神经网络(1D-CNN)模型进行水深反演,分析底质分类前后水深反演结果与不同模型的水深反演结果.结果表明,不同模型在底质分类后水深反演精度均高于底质分类前水深反演精度.Bi-LSTM模型的水深反演精度最高,底质分类后遥感影像水深反演的平均绝对误差、均方根误差与决定系数分别为0.333 m、0.474 m、0.814 m,均优于对比模型.
Remote sensing water depth inversion in shallow sea areas based on sediment classification
In recent years,water depth inversion of satellite remote sensing images has been a hot research topic in China and abroad. Previous water depth inversion models of remote sensing images are mostly based on the condition of uniform sediment,lacking research on mixed seabed sediment. To address this issue,this paper proposed a water depth inversion model of remote sensing images based on a sediment classification. Satellite images of Wuzhizhou Island and its surrounding areas around Hainan Island in China were used as experimental data. After preprocessing and sediment classification,the bidirectional long short-term memory network (Bi-LSTM) model,Stumpf model,and one dimension-convolutional neural network (1D-CNN) model were used for water depth inversion. The water depth inversion results before and after sediment classification were analyzed,as well as the water depth inversion results of different models. The results show that the water depth inversion accuracy of different models after sediment classification is higher than that before sediment classification.The Bi-LSTM model has the highest water depth inversion accuracy,and the average absolute error,root mean square error,and determination coefficient of water depth inversion of remote sensing images after sediment classification are 0. 333 m,0. 474 m,and 0. 814 m,respectively,which are better than those of the comparison model.

remote sensing imageswater depth inversionclassification of seabed sedimentbidirectional long short-term memory network( Bi-LSTM)Stumpf modelone dimension-convolutional neural network( 1D-CNN) model

王江杰、王星河

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浙江省测绘科学技术研究院,浙江杭州 310023

遥感影像 水深反演 海底底质分类 双向长短期记忆网络(Bi-LSTM) Stumpf模型 1D-CNN模型

中央引导地方科技发展基金

YDZX2022019

2024

北京测绘
北京市测绘设计研究院,北京测绘学会

北京测绘

影响因子:0.55
ISSN:1007-3000
年,卷(期):2024.38(8)