MAR20：遥感图像军用飞机目标识别数据集
MAR20： A benchmark for military aircraft recognition in remote sensing images
- 2023年27卷第12期 页码：2688-2696
纸质出版日期： 2023-12-07
DOI： 10.11834/jrs.20222139
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摘要
遥感图像军用飞机目标识别是对遥感图像中的军用飞机进行定位和细粒度分类，其在侦察预警、情报分析等领域起着至关重要的作用。但是，由于数据集匮乏，遥感图像军用飞机目标识别发展相对缓慢。为推动该领域的研究进展，本文构建了公开的遥感图像军用飞机目标识别数据集MAR20（Military Aircraft Recognition）。该数据集具有以下特点：（1）MAR20是目前规模最大的遥感图像军用飞机目标识别数据集，包含3842张图像、20种军用飞机型号以及22341个实例，并且每个目标实例具有水平边界框和有向边界框两种标注方式；（2）由于所有的细粒度类别均隶属于飞机大类，因此不同型号的飞机往往具有相似的特征，导致不同型号目标具有较高的相似性；（3）由于遥感图像采集过程中受到气候、季节、光照、遮挡、乃至大气散射等因素的影响，相同型号的目标存在较大的类内差异性。最后，为建立遥感图像军用飞机目标识别基准，本文在MAR20数据集上评估了7种常用的水平框目标识别方法和8种有向框目标识别方法。
Abstract
Military aircraft recognition in remote sensing images locates military aircraft in remote sensing images and classify them at a fine-grained level. It plays a vital role in reconnaissance and early warning
intelligence analysis
and other fields. However
the development of military aircraft recognition in remote sensing images is relatively slow due to the lack of publicly available datasets. Therefore
constructing a high-quality and large-scale military aircraft recognition dataset is important.
This study constructs a public remote sensing image military aircraft recognition dataset called MAR20 to promote the research progress in this field. The dataset has the following characteristics: (1) MAR20 is currently the largest remote sensing image military aircraft recognition dataset
which includes 3842 images
20 types
and 22341 instances. Each instance has a horizontal bounding box and also an oriented bounding box. (2) Given that all fine-grained types belong to the aircraft category
different types of aircraft often have similar characteristics
which result in high similarity of different types of targets. (3) Large intra-class differences exist between targets of the same type due to the influence of climate
season
illumination
occlusion
and even the atmospheric scattering in the process of remote sensing imaging.
To establish a benchmark for military aircraft recognition in remote sensing images
this paper study evaluates seven commonly used horizontal object recognition methods
namely
Faster R-CNN
RetinaNet
ATSS
FCOS
Cascade R-CNN
TSD
and Double-Head
as well as eight oriented object recognition methods
namely
Faster R-CNN-O
RetinaNet-O
RoI Transformer
Gliding Vertex
Double-Head-O
Oriented R-CNN
FCOS-O
and S2A-Net
on the MAR20 dataset. Through experimental comparisons in the tasks of horizontal object recognition and oriented object recognition
two-stage methods are proven to be more effective in target recognition than one-stage methods.
In this study
3842 high-resolution remote sensing images were collected from 60 military airports around the world through Google Earth
and a large-scale publicly available remote sensing image military aircraft recognition dataset
named MAR20
was established. In terms of data annotation
MAR20 provides two annotation methods
namely
horizontal bounding boxes and oriented bounding boxes
which correspond to the tasks of horizontal target recognition and oriented target recognition. We hope that the MAR20 dataset established in this study could promote the research progress in this field. MAR20 can be downloaded at
https://gcheng-nwpu.github.io/
https://gcheng-nwpu.github.io/
.
关键词
军用飞机目标识别数据集遥感图像细粒度识别
Keywords
military aircraftobject recognitiondatasetremote sensing imagesfine-grained recognition
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相关作者
程塨 西北工业大学 自动化学院
禹文奇 西北工业大学 自动化学院
王美君 西北工业大学 自动化学院
姚艳清 西北工业大学 自动化学院
谢星星 西北工业大学 自动化学院
姚西文 西北工业大学 自动化学院
韩军伟 西北工业大学 自动化学院
李峰 中国空间技术研究院 钱学森空间技术实验室
相关机构
北京市遥感信息研究所
澳大利亚西悉尼大学
中国空间技术研究院 钱学森空间技术实验室
河南大学 软件学院
西安交通大学 人工智能与机器人研究所
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