MAR20：遥感图像军用飞机目标识别数据集

禹文奇; 程塨; 王美君; 姚艳清; 谢星星; 姚西文; 韩军伟

doi:10.11834/jrs.20222139

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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
收稿：2022-03-30，

纸质出版：2023-12-07
DOI： 10.11834/jrs.20222139
稿件说明：

移动端阅览

禹文奇，程塨，王美君，姚艳清，谢星星，姚西文，韩军伟.2023.MAR20：遥感图像军用飞机目标识别数据集.遥感学报，27（12）： 2688-2696 DOI： 10.11834/jrs.20222139.

Yu W Q，Cheng G，Wang M J，Yao Y Q，Xie X X，Yao X X and Han J W. 2023. MAR20： A benchmark for military aircraft recognition in remote sensing images. National Remote Sensing Bulletin， 27（12）：2688-2696 DOI： 10.11834/jrs.20222139.

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/

关键词

Keywords

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