The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
High-resolution satellite scene classification using a sparse coding based multiple feature combination
Abstract
This article presents a new method for high-resolution satellite scene classification. Specifically, we make three main contributions: (1) we introduce the sparse coding method for satellite scene classification; (2) we present local ternary pattern histogram Fourier (LTP-HF) features, an improved rotation invariant texture descriptor based on LTPs; (3) we effectively combine a set of diverse and complementary features to further improve the performance. A two-stage linear support vector machine (SVM) classifier is designed for this purpose. In the first stage, the SVM is used to generate probability images with a scale invariant feature transform (SIFT), LTP-HF and colour histogram features, respectively. The generated probability images with different features are fused in the second stage in order to obtain the final classification results. Experimental results show that the suggested classification method achieves very promising performance.
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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 performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
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
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2918242 · Cited by 1,951
Bradbury, K., Saboo, R., L. Johnson, T., Malof, J. M., Devarajan, A., Zhang, W., M. Collins, L., & G. Newell, R. (2016) · Scientific Data · doi:10.1038/sdata.2016.106 · Cited by 624
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Metadata
- Authors
- Sheng, G., Yang, W., Xu, T., & Sun, H.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2011-10-12
- Volume
- 33
- Issue
- 8
- Pages
- 2395-2412
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2067178723
- Cited by
- 309
- References
- 6
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sheng, G., Yang, W., Xu, T., & Sun, H. (2012). High-resolution satellite scene classification using a sparse coding based multiple feature combination. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2011.608740
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
