2019 - 2021年三岛近海海洋目标可见光遥感影像数据集

Xuyang Guo, Shanshan Cao, Rui Man, Yiming Zeng, Yi Wang, Gulimila Kezierbieke, Wei Sun
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引用次数: 0

摘要

利用计算机视觉技术对近海海洋目标进行智能检测,可以为海洋行政管理、海洋环境监督管理以及海洋环境保护政策的制定提供科学依据,为经济的稳定发展提供有力的环境信息参考。该数据集以谷歌Earth为主要数据源,采集自中国福建省宁德市东南部三堆港的数据,时间跨度为2019 - 2021年。该数据集包括在不同季节、背景和光照条件下获取的1761幅可见光遥感图像,以及相应的水平目标检测标签、旋转目标检测标签和语义分割标签,涵盖船舶、鱼排网箱养殖区和筏式养殖区三种近海海洋目标类型。经过筛选和校正,可以满足当前主流深度学习模型的训练需求。该数据集可为海上目标图像的语义分割、水平目标检测、旋转目标检测等研究领域提供基础数据。
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A dataset of the visible light remote sensing images for offshore maritime targets in Sanduao from 2019 to 2021
The intelligent detection of offshore maritime targets using computer vision technology can provide a scientific basis for marine administrative management, marine environmental supervision and management as well as the formulation of marine environmental protection policies, providing a powerful environmental information reference for the steady development of the economy. The dataset includes the data collected from Sanduao Harbor in the southeast of Ningde City, Fujian Province, China, with Google Earth serving as the primary data source and a time span from 2019 to 2021. This dataset comprises 1,761 visible light remote sensing images acquired under different seasons, backgrounds and illumination conditions, and corresponding horizontal object detection labels, rotational object detection labels and semantic segmentation labels, covering three types of offshore maritime targets, namely ships, fish row cage culture areas, and raft culture areas. After screening and correction, it can meet the current mainstream deep learning model training needs. This dataset can provide basic data for the semantic segmentation, horizontal object detection, rotational object detection and other research fields of offshore maritime target images.
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