The DOI matches its Crossref record; 53 of the 55 references it cites were found in a registry or an index.
A light and faster regional convolutional neural network for object detection in optical remote sensing images
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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)
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
(2014) · Cited by 33,419
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Research works printed but not linked: 8
- Baldi, P., Sadowski, P., 2013. Understanding Dropout, Neural Information Processing Systems.
- Glorot, X., Bordes, A., Bengio, Y., 2011. Deep Sparse Rectifier Neural Networks, AISTATS, pp. 315–323.
- Goodfellow, I.J., Wardefarley, D., Mirza, M., Courville, A., Bengio, Y., 2013. Maxout Networks, International Conference on Machine Learning.
- Gulcehre, C., Moczulski, M., Denil, M., Bengio, Y., 2016. Noisy Activation Functions, International Conference on Machine Learning.
- Ioffe, S., Szegedy, C., 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning.
- Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. ImageNet Classification with Deep Convolutional Neural Networks, Neural Information Processing Systems, pp. 1097–1105.
- Krogh, A., Hertz, J.A., 1992. A simple weight decay can improve generalization, Neural Information Processing Systems.
- Wang, N., Yeung, D., 2013. Learning a deep compact image representation for visual tracking, neural information processing systems, pp. 809–817.
Cited by
Sorted by citations
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
Sun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 585
AlMansour, N., Syed, H. F., Khayat, N. R., Al-Theeb, R., Juri, R. E., Alhiyafi, J. A., Alrashed, S., & Olatunji, S. O. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.017 · Cited by 325
Computer vision applications in construction safety assurance
Fang, W., Ding, L., Love, P. E. D., Luo, H., Li, H., Pena-Mora, F. A., Zhong, B., & Zhou, C. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.103013 · Cited by 328
Yan, X., Ai, T., Yang, M. J., & Yin, H. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.02.010 · Cited by 280
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
