The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images
Abstract
Most of the existing deep-learning-based methods are difficult to effectively deal with the challenges faced for geospatial object detection such as rotation variations and appearance ambiguity. To address these problems, this paper proposes a novel deep-learning-based object detection framework including region proposal network (RPN) and local-contextual feature fusion network designed for remote sensing images. Specifically, the RPN includes additional multiangle anchors besides the conventional multiscale and multiaspect-ratio ones, and thus can deal with the multiangle and multiscale characteristics of geospatial objects. To address the appearance ambiguity problem, we propose a double-channel feature fusion network that can learn local and contextual properties along two independent pathways. The two kinds of features are later combined in the final layers of processing in order to form a powerful joint representation. Comprehensive evaluations on a publicly available ten-class object detection data set demonstrate the effectiveness of the proposed method.
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Cites
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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A fast learning algorithm for deep belief nets
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Self-citationObject Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
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Cited by
Object detection in optical remote sensing images: A survey and a new benchmark
Li, K., Wan, G., Cheng, G., Meng, L., & Han, J. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.023 · Cited by 2,317
Self-citationA review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584
Deep learning for unmanned aerial vehicle-based object detection and tracking: a survey
Wu, X., Li, W., Hong, D., Tao, R., & Du, Q. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3115137 · Cited by 421
Cheng, G., Han, J., Zhou, P., & Xu, D. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2867198 · Cited by 408
Self-citationRingMo: A Remote Sensing Foundation Model With Masked Image Modeling
Sun, X., Wang, P., Lu, W., Zhu, Z., Lu, X., He, Q., Li, J., Rong, X., Yang, Z., Chang, H., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3194732 · Cited by 323
Metadata
- Authors
- Li, K., Cheng, G., Bu, S., & You, X.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2018-04
- Volume
- 56
- Issue
- 4
- Pages
- 2337-2348
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2779335303
- Cited by
- 439
- References
- 10
- FWCI
- 15.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, K., Cheng, G., Bu, S., & You, X. (2018). Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2017.2778300
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