Object detection in aerial images using DOTA dataset: A survey

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal Pub Date : 2024-10-20 DOI:10.1016/j.jag.2024.104208

Ziyi Chen , Huayou Wang , Xinyuan Wu , Jing Wang , Xinrui Lin , Cheng Wang , Kyle Gao , Michael Chapman , Dilong Li

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引用次数: 0

Abstract

In recent years, the Dataset for Object deTection in Aerial images (DOTA) dataset has played a pivotal role in advancing object detection in aerial images (ODAI). Despite its significance, there hasn’t been a comprehensive review summarizing its research developments. Addressing this gap, this paper offers the first comprehensive overview on the subject. Within this review, we begin by examining prevalent object detection datasets of natural scene images alongside object detection datasets of remote sensing images (RSIs). We then present an in-depth comparative analysis between these datasets and the DOTA dataset, supported by numerous charts and tables. We proceed to outline both traditional techniques for ODAI and methods rooted in deep learning. Subsequently, we provide a recap of the latest advancements in the field achieved using the DOTA dataset. Concluding our review, we delve into the current challenges facing ODAI and propose potential future research directions.

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使用 DOTA 数据集检测航空图像中的物体：一项调查

近年来，航空图像物体检测数据集（DOTA）在推进航空图像物体检测（ODAI）方面发挥了关键作用。尽管其重要性不言而喻，但一直没有对其研究进展进行全面总结。针对这一空白，本文首次对该主题进行了全面综述。在这篇综述中，我们首先研究了自然场景图像的常见物体检测数据集和遥感图像（RSI）的物体检测数据集。然后，我们通过大量图表对这些数据集和 DOTA 数据集进行了深入的比较分析。接下来，我们将概述 ODAI 的传统技术和基于深度学习的方法。随后，我们回顾了该领域利用 DOTA 数据集取得的最新进展。在回顾的最后，我们深入探讨了 ODAI 当前面临的挑战，并提出了潜在的未来研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences

CiteScore

12.00

自引率

0.00%

发文量

审稿时长

77 days

期刊介绍： The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.