The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters
Abstract
A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.
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Cites
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Recent advances in techniques for hyperspectral image processing
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Self-citationDetection algorithms for hyperspectral imaging applications
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Research works printed but not linked: 1
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Cited by
Deep learning classifiers for hyperspectral imaging: A review
Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.09.006 · Cited by 1,073
BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection
Wang, D., Zhuang, L., Gao, L., Sun, X., Huang, M., & Plaza, A. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3335484 · Cited by 579
Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser
Dian, R., Li, S., & Kang, X. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2980398 · Cited by 328
Self-citationHyperspectral Anomaly Detection by Fractional Fourier Entropy
Tao, R., Zhao, X., Li, W., Li, H.-C., & Du, Q. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2940278 · Cited by 264
Jiang, T., Li, Y., Xie, W., & Du, Q. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2965961 · Cited by 209
Metadata
- Authors
- Kang, X., Zhang, X., Li, S., Li, K., Li, J., & Benediktsson, J. A.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2017-10
- Volume
- 55
- Issue
- 10
- Pages
- 5600-5611
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2740976805
- Cited by
- 469
- References
- 41
- FWCI
- 25.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Funders
- National Natural Science Foundation of China (61601179)
- National Natural Science Fund of China for Distinguished Young Scholars (61325007)
- National Natural Science Fund of China for International Cooperation and Exchanges (615201060011)
- Science and Technology Plan Projects Fund of Hunan Province (2015WK3001)
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- Kang, X., Zhang, X., Li, S., Li, K., Li, J., & Benediktsson, J. A. (2017). Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2017.2710145
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