The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Multi-class geospatial object detection and geographic image classification based on collection of part detectors
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In the citing paper
“or to train independent detectors separately for each orientation [167, 168].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [168]
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
A Bayesian Hierarchical Model for Learning Natural Scene Categories
Fei-Fei, L., & Perona, P. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.16 · Cited by 3,665
Bag-of-visual-words and spatial extensions for land-use classification
Yang, Y., & Newsam, S. D. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869829 · Cited by 3,183
Poselets: Body part detectors trained using 3D human pose annotations
Bourdev, L., & Malik, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459303 · Cited by 961
Ensemble of exemplar-SVMs for object detection and beyond
Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126229 · Cited by 865
Cited by
Sorted by citations
Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689
“The most straightforward solution to this problem is to perform data augmentation so that an object in any orientation can be well covered by the augmented data distribution [166], or to train independent detectors separately for each orientation [167, 168].”
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
Self-citationAID: A benchmark data set for performance evaluation of aerial scene classification
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2685945 · Cited by 2,372
“Due to the drastically increasing number of aerial images and the highly complex geometrical structures and spatial patterns, to effectively understand the semantic content of them is particularly important, driven by many real-world applications in remote sensing community.”
BackgroundObject 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
“This dataset has been widely used in the earth observation community (Cheng et al., 2014; Cheng et al., 2018b; Farooq et al., 2017; Guo et al., 2018; Han et al., 2017a; Li et al., 2018; Yang et al., 2018b; Yang et al., 2017; Zhong et al., 2018).”
BackgroundMethodSelf-citationCheng, G., Zhou, P., & Han, J. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2601622 · Cited by 1,771
“Specifically, our RICNN with fine-tuning obtained 48.06%, 41.82%, 47.89%, 18.05%, 12.92%, and 10.86% performance gains, in terms of mean AP over all ten object categories, compared with the BoW-based method [12], the SSCBoW-based method [13], the FDDL-based method [16], the COPD-based method [22], the transferredCNN-m…”
BackgroundMethodInfluentialSelf-citation
Metadata
- Authors
- Cheng, G., Han, J., Zhou, P., & Guo, L.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Han, Junwei
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 98
- Pages
- 119-132
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2005368619
- Cited by
- 1,007
- References
- 38
- Citations by year
- 2026: 115 · 2025: 152 · 2024: 141 · 2023: 111 · 2022: 105 · 2021: 105 · 2020: 87 · 2019: 64 · 2018: 41 · 2017: 44 · 2016: 26 · 2015: 16
- FWCI
- 43.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- Funders
- Ministry of Education of the People's Republic of China (20136102110037)
- National Natural Science Foundation of China (61401357, 61333017, 61473231, 91120005)
- China Postdoctoral Science Foundation (2014M552491)
- Topics
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- geospatial object detection, linear support vector machine, spatial pattern detection, remote sensing images, image representation
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