The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A support vector method for anomaly detection in hyperspectral imagery
Abstract
This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery
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Cites
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Hyperspectral image data analysis
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Analysis of CFAR processors in nonhomogeneous background
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Anomaly detection from hyperspectral imagery
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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
Collaborative representation for hyperspectral anomaly detection
Li, W., & Du, Q. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2343955 · Cited by 758
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation
Xu, Y., Wu, Z., Li, J., Plaza, A., & Wei, Z. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2493201 · Cited by 602
Hyperspectral Target Detection : An Overview of Current and Future Challenges
Nasrabadi, N. M. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2278992 · Cited by 580
Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection
Cheng, T., & Wang, B. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2936609 · Cited by 266
Metadata
- Authors
- Banerjee, A., Burlina, P., & Diehl, C.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2006-08
- Volume
- 44
- Issue
- 8
- Pages
- 2282-2291
- ISSN
- 0196-2892 (ISSN-L)
- OpenAlex
- W2165447611
- Cited by
- 384
- References
- 15
- FWCI
- 43.60
- Type
- journal article
- Language
- en
- Open access
- closed
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