Object detection in optical remote sensing images: A survey and a new benchmark
Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of th...
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| Published in | ISPRS journal of photogrammetry and remote sensing Vol. 159; pp. 296 - 307 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-2716 1872-8235 |
| DOI | 10.1016/j.isprsjprs.2019.11.023 |
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| Abstract | Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23,463 images and 192,472 instances, covering 20 object classes. The proposed DIOR dataset (1) is large-scale on the object categories, on the object instance number, and on the total image number; (2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter- and intra-class size variability across objects; (3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and (4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-the-art approaches on our DIOR dataset to establish a baseline for future research. |
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| AbstractList | Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23,463 images and 192,472 instances, covering 20 object classes. The proposed DIOR dataset (1) is large-scale on the object categories, on the object instance number, and on the total image number; (2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter- and intra-class size variability across objects; (3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and (4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-the-art approaches on our DIOR dataset to establish a baseline for future research. |
| Author | Wan, Gang Meng, Liqiu Cheng, Gong Han, Junwei Li, Ke |
| Author_xml | – sequence: 1 givenname: Ke orcidid: 0000-0002-7873-1554 surname: Li fullname: Li, Ke organization: Zhengzhou Institute of Surveying and Mapping, Zhengzhou 450052, China – sequence: 2 givenname: Gang surname: Wan fullname: Wan, Gang organization: Zhengzhou Institute of Surveying and Mapping, Zhengzhou 450052, China – sequence: 3 givenname: Gong orcidid: 0000-0001-5030-0683 surname: Cheng fullname: Cheng, Gong email: gcheng@nwpu.edu.cn organization: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China – sequence: 4 givenname: Liqiu surname: Meng fullname: Meng, Liqiu organization: Department of Cartography, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany – sequence: 5 givenname: Junwei orcidid: 0000-0001-5545-7217 surname: Han fullname: Han, Junwei email: junweihan2010@gmail.com organization: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China |
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| ContentType | Journal Article |
| Copyright | 2019 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) |
| Copyright_xml | – notice: 2019 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.isprsjprs.2019.11.023 |
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| Discipline | Geography Engineering |
| EISSN | 1872-8235 |
| EndPage | 307 |
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| Title | Object detection in optical remote sensing images: A survey and a new benchmark |
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