The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning
Abstract
This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of two main steps. First, we use a multinomial logistic regression (MLR) model to learn the class posterior probability distributions. This is done by using a recently introduced logistic regression via splitting and augmented Lagrangian algorithm. Second, we use the information acquired in the previous step to segment the hyperspectral image using a multilevel logistic prior that encodes the spatial information. In order to reduce the cost of acquiring large training sets, active learning is performed based on the MLR posterior probabilities. Another contribution of this paper is the introduction of a new active sampling approach, called modified breaking ties, which is able to provide an unbiased sampling. Furthermore, we have implemented our proposed method in an efficient way. For instance, in order to obtain the time-consuming maximum a posteriori segmentation, we use the α-expansion min-cut-based integer optimization algorithm. The state-of-the-art performance of the proposed approach is illustrated using both simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral image analysis methods.
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Cited by
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Self-citationSpectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
Kang, X., Li, S., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2264508 · Cited by 704
Hyperspectral Image Classification With Deep Feature Fusion Network
Song, W., Li, S., Fang, L., & Lu, T. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2794326 · Cited by 574
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation
Fang, L., Li, S., Kang, X., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2318058 · Cited by 321
Imani, M., & Ghassemian, H. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.007 · Cited by 319
Metadata
- Authors
- Li, J., Bioucas-Dias, J. M., & Plaza, A.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2011-10
- Volume
- 49
- Issue
- 10
- Pages
- 3947-3960
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2101365302
- Cited by
- 417
- References
- 47
- FWCI
- 68.40
- Type
- journal article
- Language
- en
- Open access
- closed
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