предыдущий|следующий

MAR20： A benchmark for military aircraft recognition in remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

  • Email：1884716321@163.com
  • Introduction：禹文奇， 研究方向为高分辨率遥感图像理解。E-mail： 1884716321@163.com
YU Wenqi,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

  • Email：gcheng@nwpu.edu.cn
  • Introduction：程塨， 研究方向为高分辨率遥感图像理解。E-mail： gcheng@nwpu.edu.cn
CHENG Gong*,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

WANG Meijun,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

YAO Yanqing,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

XIE Xingxing,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

YAO Xiwen,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

HAN Junwei

реферат

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 athttps://gcheng-nwpu.github.io/.

ключеви́че слова́

Military aircraft;object recognition;dataset;remote sensing images;Fine-grained recognition

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