文献类型：期刊文献

英文题名：Marine Equipment Siting Using Machine-Learning-Based Ocean Remote Sensing Data: Current Status and Future Prospects

作者：Zhang, Dapeng[1];Ma, Yunsheng[1,2];Zhang, Huiling[3];Zhang, Yi[1]

机构：[1]Guangdong Ocean Univ, Ship & Maritime Coll, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 316021, Peoples R China;[3]Guangdong Ocean Univ, Coll Ocean Engn & Energy, Zhanjiang 524088, Peoples R China

年份：2024

卷号：16

期号：20

外文期刊名：SUSTAINABILITY

收录：SSCI(收录号：WOS:001341545600001)、SCI-EXPANDED(收录号：WOS:001341545600001)、、Scopus(收录号：2-s2.0-85207353252)、WOS

基金：This work was funded by the National Science Foundation of China (No. 42276070).

语种：英文

外文关键词：ocean remote sensing; machine learning; site selection; data requirements; bibliometrics; cluster analysis

外文摘要：As the global climate changes, there is an increasing focus on the oceans and their protection and exploitation. However, the exploration of the oceans necessitates the construction of marine equipment, and the siting of such equipment has become a significant challenge. With the ongoing development of computers, machine learning using remote sensing data has proven to be an effective solution to this problem. This paper reviews the history of remote sensing technology, introduces the conditions required for site selection through measurement analysis, and uses cluster analysis methods to identify areas such as machine learning as a research hotspot for ocean remote sensing. The paper aims to integrate machine learning into ocean remote sensing. Through the review and discussion of this article, limitations and shortcomings of the current stage of ocean remote sensing are identified, and relevant development proposals are put forward.