The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Multiview Deep Learning for Land-Use Classification
Open accessAbstract
A multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification, and it is validated on a well-known data set. The hypothesis that simultaneous multiscale views can improve composition-based inference of classes containing size-varying objects compared to single-scale multiview is investigated. The end-to-end learning system learns a hierarchical feature representation with the aid of convolutional layers to shift the burden of feature determination from hand-engineering to a deep convolutional neural network (DCNN). This allows the classifier to obtain problem-specific features that are optimal for minimizing the multinomial logistic regression objective, as opposed to user-defined features which trade optimality for generality. A heuristic approach to the optimization of the DCNN hyperparameters is used, based on empirical performance evidence. It is shown that a single DCNN can be trained simultaneously with multiscale views to improve prediction accuracy over multiple single-scale views. Competitive performance is achieved for the UC Merced data set, where the 93.48% accuracy of multiview deep learning outperforms the 85.37% accuracy of SIFT-based methods and the 90.26% accuracy of unsupervised feature learning.
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Citations
Cites
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Cited by
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
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
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
AID: A benchmark data set for performance evaluation of aerial scene classification
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EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
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Convolutional neural networks for large-scale remote-sensing image classification
Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2612821 · Cited by 1,114
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Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention
Alhichri, H., Alswayed, A. S., Bazi, Y., Ammour, N., & Alajlan, N. A. (2021) · IEEE Access · doi:10.1109/access.2021.3051085 · Cited by 357
Siamese-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
- Luus, F. P. S., Salmon, B. P., van den Bergh, F., & Maharaj, B. T. J.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2015-12
- Volume
- 12
- Issue
- 12
- Pages
- 2448-2452
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2015386604
- Cited by
- 257
- References
- 4
- FWCI
- 45.50
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- National Research Foundation of South Africa
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