基于遥感的海洋三维温盐场智能探测研究进展
CSTR:
作者:
作者单位:

作者简介:

杨俊钢(1980-),男,博士,研究员,主要从事海洋动力环境遥感研究。

通讯作者:

中图分类号:

P733.1

基金项目:

国家自然科学基金项目“基于海洋表层卫星遥感观测的海洋水下动力环境智能探测方法研究”(62231028)


Research Progress on Intelligent Detection of 3D Ocean Temperature and Salinity Based on Remote Sensing
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    海洋三维温盐场信息是描述海洋物理属性特征和掌握海洋物理运动过程的重要参数,获取准确的海洋三维温度和盐度信息对于认识海洋、开发利用海洋和海洋科学研究等具有重要意义。随着人工智能与深度学习方法的发展,采用深度学习方法的海洋次表层三维温盐场智能探测研究成为热点之一。从海洋温盐观测数据集、传统机器学习方法三维温盐智能探测、一般神经网络三维温盐智能探测和深度学习三维温盐智能探测等方面展开,对与海洋三维温盐场智能探测相关的研究进展进行综述,最后针对三维温盐场智能探测存在的问题和未来的发展趋势进行了总结和展望。

    Abstract:

    The 3D(three-dimensional)temperature and salinity field information of the ocean is an important parameter for describing the physical properties of the ocean and mastering the process of ocean physical movement. Obtaining accurate 3D temperature and salinity information of the ocean is of great significance for understanding, developing and utilizing the ocean,and marine scientific research. With the development of artificial intelligence and deep learning methods,intelligent detection of subsurface 3D temperature and salinity fields using deep learning methods has become one of the hotspots. This article reviews the research progress related to intelligent detection of 3D temperature and salinity fields of the ocean from the perspectives of ocean temperature and salinity observation datasets,traditional machine learning-based 3D temperature and salinity intelligent detection,general neural network-based 3D temperature and salinity intelligent detection,and deep learning-based 3D temperature and salinity intelligent detection. Finally , the problems and future development trends of intelligent detection of 3D temperature-salinity fields are summarized and prospected.

    参考文献
    相似文献
    引证文献
引用本文

杨俊钢,穆佳栋,谷浩然,等.基于遥感的海洋三维温盐场智能探测研究进展[J].数字海洋与水下攻防,2024,7(1):9-19

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-12-13
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-03-01
  • 出版日期:
文章二维码