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Received：01 November 2023，
Published：07 December 2023
移动端阅览
近年来，随着深度学习等人工智能技术在光学遥感目标检测领域中的快速发展，大量相关研究算法不断涌现，逐渐形成了一种基于数据驱动的光学遥感图像目标检测新范式。高质量的遥感数据成为了此类范式算法研究的前置条件和必要储备，遥感数据的重要性日益凸显。迄今为止，国内外各大研究机构已相继发布了数量众多且规模不一的光学遥感图像目标检测数据集，为基于深度学习的遥感图像目标检测算法的发展奠定了研究基础。然而，当前尚未有相关学者对已发布的光学遥感图像检测数据集进行全面的归纳整理与分析，针对此问题，本文全面调研领域文献，对2008年—2023年期间已发布的公开光学遥感图像检测数据集进行整合分析，并依据不同的数据标注方式进行划分，对其中的11个典型数据集进行了全面阐述，以表格的形式对所有的数据集信息进行归纳总结，同时采用3种分析方式去描述数据集的发展情况，即：元数据分析，从数量分布、地域分布、来源分布、规模分布着手；分辨率分析，从空间分辨率与光谱分辨率着手；基本信息分析，从类别数量、图像数量、实例数量及图像宽度信息着手，有效论证了光学遥感图像目标检测数据集必然朝着高质量、大规模、多类别的方向发展。此外，针对已发布的数据集，从水平框目标检测、旋转框目标检测以及细分检测方向（小目标检测和细粒度检测）等多个角度对相关算法的应用和发展进行了概述，证实了遥感数据对目标检测算法的研究具有积极的推动作用。综上，本文将为基于深度学习的目标检测算法在遥感领域的应用提供参考。
With the introduction of artificial-intelligence technologies such as deep learning into the field of optical remote-sensing detection
various algorithms have emerged. The use of these algorithms has gradually formed a new paradigm of data-driven optical remote-sensing image object detection. Consequently
high-quality remote-sensing data has become a prerequisite and a necessary resource for researching these paradigm algorithms. highlighting the increasing importance of remote-sensing data. To date
numerous optical remote-sensing image object detection datasets have been published by major research institutions domestically and internationally. These datasets have laid the foundation for the development of deep learning-based remote-sensing image detection tasks. However
no comprehensive summarization and analysis of the published optical remote-sensing image detection datasets have been conducted by scholars. Therefore
this paper aimed to provide a comprehensive review of the published datasets and an overview of algorithm applications. We also aimed to provide a reference for subsequent research in related fields.
This paper presents an overview and synthesis of the optical remote-sensing image object detection datasets published between 2008 and 2023. The synthesis is based on an extensive and comprehensive survey of literature in the field. By reviewing and analyzing these datasets
we enable a comprehensive understanding of the progress and trends in optical remote-sensing image object detection dataset research.
This paper categorizes the optical remote-sensing image object detection datasets published from 2008 to 2023 based on the annotation method. A comprehensive description of 11 representative datasets is provided
and all dataset information are summarized in tabular form. The analysis considers the information in the datasets themselves and also the spatial and spectral resolution of the images in the datasets. Other basic information including the number of categories
number of images
number of instances
and image-width information are also considered. This analysis effectively demonstrates the trend toward high quality
large scale
and multi-category development of object-detection datasets for optical remote-sensing images. Additionally
we provide an overview of the development and application of algorithms related to published datasets from different perspectives (e.g.
horizontal bounding box object detection and rotated bounding box object detection)
as well as a subdivision of detection directions (e.g.
small object detection and fine-grained detection). Our findings confirm the influential role of remote-sensing data in driving algorithmic advances.
In summary
we offer a comprehensive review of optical remote-sensing image object detection datasets from various perspectives. To our best knowledge
this comprehensive review is the first one on such datasets in the field. The work serves as a valuable reference for subsequent research on deep learning-based optical remote-sensing image object detection
providing insights into data availability and research directions. This study is expected to contribute to the advancement of this field by offering a solid foundation for further investigation and innovation.
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