The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification
Open accessAbstract
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Cited by
Hyperspectral remote sensing data analysis and future challenges
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Domain adaptation for the classification of remote sensing data: An overview of recent advances
Tuia, D., Persello, C., & Bruzzone, L. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2548504 · Cited by 675
Self-citationOn Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2816 · Cited by 160
Metadata
- Authors
- Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Munoz-Mari, J.
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2011-06
- Volume
- 5
- Issue
- 3
- Pages
- 606-617
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W2107131609
- Cited by
- 558
- References
- 3
- FWCI
- 69.16
- Type
- journal article
- Language
- en
- Open access
- green
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