The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Land-Use Scene Classification Using a Concentric Circle-Structured Multiscale Bag-of-Visual-Words Model
Abstract
High-resolution remote sensing image-based land-use scene classification is a difficult task, which is to recognize the semantic category of a given land-use scene image based on priori knowledge. Land-use scenes often cover multiple land-cover classes or ground objects, which makes a scene very complex and difficult to represent and recognize. To deal with this problem, this paper applies the well-known bag-of-visual-words (BOVWs) model which has been very successful in natural image scene classification. Moreover, many existing BOVW methods only use scale-invariant feature transform (SIFT) features to construct visual vocabularies, lacking in investigation of other features or feature combinations, and they are also sensitive to the rotation of image scenes. Therefore, this paper presents a concentric circle-based spatial-rotation-invariant representation strategy for describing spatial information of visual words and proposes a concentric circle-structured multiscale BOVW method using multiple features for land-use scene classification. Experiments on public land-use scene classification datasets demonstrate that the proposed method is superior to many existing BOVW methods and is very suitable to solve the land-use scene classification problem.
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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
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
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
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons
Leung, T., & Malik, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011126920638 · Cited by 1,498 · cited 1×
“In recent years, the advent of the bag-of-visual-words (BOVWs) model, which received much concern in the field of image analysis and classification [12]–[18], has provided an efficient means to solve the problem of remote sensing imagebased land-use scene classification.”
Background
Cited by
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
AID: 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
Huang, B., Zhao, B., & Song, Y. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.04.050 · Cited by 591
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
Zhao, B., Zhong, Y., Xia, G.-S., & Zhang, L. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2496185 · Cited by 361
Metadata
- Authors
- Zhao, L.-J., Tang, P., & Huo, L.-Z.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2014-12
- Volume
- 7
- Issue
- 12
- Pages
- 4620-4631
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W1968591910
- Cited by
- 174
- References
- 7
- FWCI
- 15.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhao, L.-J., Tang, P., & Huo, L.-Z. (2014). Land-Use Scene Classification Using a Concentric Circle-Structured Multiscale Bag-of-Visual-Words Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2014.2339842
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