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在计算机视觉领域，随着深度学习算法不断发展，目标识别模型的性能得以显著提高，但是针对高分辨率遥感影像中的小目标识别仍具有挑战性，特别是海上小型作业船舶的识别难度较高，而船舶识别在安全、交通、军事等领域重要性日渐突出。基于此，对用于目标识别的经典数据集以及用于船舶识别的数据集进行了介绍，详细分析了目标识别的模型的特征及优缺点，并进行了对比分析，展望了深度学习在船舶遥感识别的未来研究方法，提出多模态融合技术的应用。
Abstract：The continuous development of deep learning algorithms let the ability and efficiency of target identification models improved significantly in the study field of computer vision. However, the identification of small targets in high-resolution remote sensing images remains challenging, especially for small operating ships. With the development of marine economic, the identification of ships becoming more and more important in different areas, such as maritime safety, transportation and military. We discussed the classic data sets used for target identification and ship identification in the present study, studied and contrastive analyzed the characteristics, advantages and disadvantages of target identification model in detail, prospected the future research methods of deep learning in ship remote sensing identification, and put forward the application of multi-modal fusion technology.
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基本信息:
中图分类号:TP18;TP751;U675.79
引用信息:
[1]陈秋,邵长高,吕建军.基于深度学习的海上船舶遥感识别方法对比分析[J].地理空间信息,2024,22(12):74-78.
基金信息:
三亚崖州湾科技城管理局2022年度科技计划资助项目（SKJC-2022-01-001）; 海域监测应用资助项目（2022R-SYS25-03）
2024-12-24
2024-12-24