The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images
Abstract
Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elementsoriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR landuse data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.
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Cites
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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
Learning representations by back-propagating errors
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Object Detection with Discriminatively Trained Part-Based Models
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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
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Self-citation
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
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
Cheng, G., Zhou, P., & Han, J. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2601622 · Cited by 1,771
Self-citationBall, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Huang, B., Zhao, B., & Song, Y. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.04.050 · Cited by 591
Cheng, G., Zhou, P., & Han, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.315 · Cited by 175
Self-citationSiamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization
Bashmal, L., Bazi, Y., AlHichri, H., AlRahhal, M., Ammour, N., & Alajlan, N. (2018) · Remote Sensing · doi:10.3390/rs10020351 · Cited by 60
Metadata
- Authors
- Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2015-08
- Volume
- 53
- Issue
- 8
- Pages
- 4238-4249
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2070452328
- Cited by
- 305
- References
- 6
- FWCI
- 79.23
- Type
- journal article
- Language
- en
- Open access
- closed
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