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A light and faster regional convolutional neural network for object detection in optical remote sensing images
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“Ding et al. (2018) enhanced the structure of the base VGG16-Net algorithm for improving the precision of airplane and car detection”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Ding et al. (2018)
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Metadata
- Authors
- Ding, P., Zhang, Y., Deng, W.-J., Jia, P., & Kuijper, A.
- Affiliations
- Chinese Academy of Sciences (CN)
- Fraunhofer Institute for Computer Graphics Research (DE)
- Corresponding author
- Ding, Peng
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 141
- Pages
- 208-218
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2803867573
- Cited by
- 174
- References
- 55
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 12 · 2023: 27 · 2022: 26 · 2021: 33 · 2020: 39 · 2019: 24 · 2018: 4
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object detection, optical remote sensing images, satellite imagery, Faster R-CNN, VGG16, dense convolutional network, multi-scale representation, memory reduction, aircraft detection, small object detection
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