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FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

文献类型：期刊论文

作者Sun, Xian2,3,4; Wang, Peijin2,3,4; Yan, Zhiyuan2,3,4; Xu, Feng5; Wang, Ruiping6; Diao, Wenhui2,3,4; Chen, Jin7; Li, Jihao2,3,4; Feng, Yingchao2,3,4; Xu, Tao2,3,4
刊名ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
出版日期2022-02-01
卷号184页码:116-130
ISSN号0924-2716
关键词Remote sensing images Fine-grained object detection and recognition Deep learning Benchmark dataset Convolutional neural network (CNN)
DOI10.1016/j.isprsjprs.2021.12.004
英文摘要With the rapid development of deep learning, many deep learning-based approaches have made great achievements in object detection tasks. It is generally known that deep learning is a data-driven approach. Data directly impact the performance of object detectors to some extent. Although existing datasets include common objects in remote sensing images, they still have some scale, category, and image limitations. Therefore, there is a strong requirement for establishing a large-scale object detection benchmark for high-resolution remote sensing images. In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 40,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M. We collected remote sensing images with a resolution of 0.3 m to 0.8 m from different platforms, which are spread across many countries and regions. All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 subcategories by oriented bounding boxes. Compared with existing detection datasets that are dedicated to object detection, the FAIR1M dataset has 4 particular characteristics: (1) it is much larger than other existing object detection datasets both in terms of the number of instances and the number of images, (2) it provides richer fine-grained category information for objects in remote sensing images, (3) it contains geographic information such as latitude, longitude and resolution attributes, and (4) it provides better image quality due to the use of a careful data cleaning procedure. Based on the FAIR1M dataset, we propose three fine-grained object detection and recognition tasks. Moreover, we evaluate several state-of-the-art approaches to establish baselines for future research. Experimental results indicate that the FAIR1M dataset effectively represents real remote sensing applications and is quite challenging for existing methods. Considering the fine-grained characteristics, we improve the evaluation metric and introduce the idea of hierarchy detection into the algorithms. We believe that the FAIR1M dataset will contribute to the earth observation community via fine-grained object detection in large-scale real-world scenes. FAIR1M Website: http://gaofen-challenge.com/.
资助项目ISPRS Scientific Initiatives 2021 ; National Natural Science Foundation of China[61725105] ; Major Project of the China High-resolution Earth Observation System[GFZX0404120201/GFZX0404120205] ; [2021]
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者ELSEVIER
WOS记录号WOS:000784317000002
源URL[http://119.78.100.204/handle/2XEOYT63/18904]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Cheng; Fu, Kun
作者单位1.Xiamen Univ, Sch Informat Sci & Engn, Fujian Key Lab Sensing & Comp Smart Cities, Xiamen 361005, Peoples R China
2.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Elect, Elect & Commun Engn, Beijing 100049, Peoples R China
4.Aerosp Informat Res Inst, Chinese Acad Sci, Key Lab Network Informat Syst Technol NIST, Beijing 100190, Peoples R China
5.Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Shanghai, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
7.Beijing Remote Sensing Informat Inst, Beijing 100011, Peoples R China
8.Inst Photogrammetry & Remote Sensing, Karlsruhe Inst Technol, Karlsruhe, Germany
9.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Xian,Wang, Peijin,Yan, Zhiyuan,et al. FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2022,184:116-130.
APA Sun, Xian.,Wang, Peijin.,Yan, Zhiyuan.,Xu, Feng.,Wang, Ruiping.,...&Fu, Kun.(2022).FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,184,116-130.
MLA Sun, Xian,et al."FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 184(2022):116-130.

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来源：计算技术研究所

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