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热带海洋学报

• • 上一篇    

基于SDW-LPT的多时相遥感影像融合及浅海水深反演

贾东振1, 司小焕1, 何秀凤1, 徐宗浩1, 凌金平2   

  1. 1. 河海大学地球科学与工程学院, 江苏 南京 211100;

    2. 镇江市长江河道管理处, 江苏 镇江 212008;



  • 收稿日期:2025-10-28 修回日期:2025-12-17 接受日期:2025-12-26
  • 通讯作者: 贾东振
  • 基金资助:
    国家自然科学基金(41474001;42304053)

Shallow water bathymetry inversion using SDW-LPT-based multi-temporal remote sensing imagery fusion

JIA Dongzhen1, SI Xiaohuan1, HE Xiufeng1, XU Zonghao1, LING Jinping2    

  1. 1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China;

    2. Zhenjiang Yangtze River Channel Management Office, ZhenJiang 212008, China;


  • Received:2025-10-28 Revised:2025-12-17 Accepted:2025-12-26
  • Supported by:

    National Natural Science Foundation of China (41474001; 42304053)

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摘要： 卫星驱动的水深反演（Satellite-Derived Bathymetry, SDB）受云雾、波浪及近岸混合像元等噪声影响，单时相影像往往难以满足高精度水深反演的需求。为此，本文基于标准差加权拉普拉斯金字塔融合方法（Standard Deviation Weighted Laplacian Pyramid Transform, SDW-LPT），构建基于ICESat-2光子数据与多时相Sentinel-2影像的SDB框架。以南海永乐环礁为例，分别采用SDW-LPT、中位数合成法与最大离群度去除法生成融合影像，并基于多波段、对数比值和二次多项式比值三类典型水深反演模型，评估各策略在0-20 m深度范围内的SDB精度。结果表明，SDW-LPT融合影像在上述三类模型下的RMSE分别为0.45 m、0.68 m和0.52 m，均优于对照方法。同时，随着融合影像数量的增加，反演精度呈持续提升趋势；当融合影像数量达到20景以上时，RMSE收敛至稳定水平；在少时相条件下，SDW-LPT仍能维持较高的精度，显示出较强的抗噪能力。研究证明了SDW-LPT在遥感水深反演中的有效性，为多源数据融合驱动的浅海水深反演提供了可行路径。

关键词: 多光谱遥感, 水深反演, 多时相, 标准差加权, 拉普拉斯金字塔变换

Abstract: Satellite-Derived Bathymetry (SDB) is often affected by noise from cloud cover, surface waves, and nearshore mixed pixels, which limits the ability of single-date imagery to achieve high-precision depth retrieval. To address this issue, this study proposes an SDB framework based on the Standard Deviation Weighted Laplacian Pyramid Transform (SDW-LPT), integrating ICESat-2 photon data with multi-temporal Sentinel-2 imagery. Taking Yongle Atoll in the South China Sea as a case study, three image fusion strategies—SDW-LPT, median-based compositing, and extreme outlier removal—were employed to generate enhanced reflectance products. Subsequently, three representative SDB models (the multi-band model, logarithmic ratio model, and quadratic polynomial ratio model) were applied to assess retrieval accuracy within the 0-20 m depth range. Results indicate that the SDW-LPT fusion imagery achieved RMSEs of 0.45 m, 0.68 m, and 0.52 m across the respective models, consistently outperforming the baseline approaches. Moreover, as the number of fused images increased, inversion accuracy improved steadily and stabilized once more than 20 images were integrated. Even under limited-temporal conditions, SDW-LPT sustained a high level of accuracy, demonstrating strong robustness and noise resistance. Overall, this study validates the effectiveness of SDW-LPT for SDB applications and offers a viable framework for multi-source data-driven shallow water bathymetry retrieval.

Key words: multispectral remote sensing, bathymetry inversion, multi-temporal imagery, standard deviation weighting, Laplacian pyramid transform