The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Hyperspectral Anomaly Detection by Fractional Fourier Entropy
Abstract
Anomaly detection is an important task in hyperspectral remote sensing. Most widely used detectors, such as Reed-Xiaoli (RX), have been developed only using original spectral signatures, which may lack the capability of signal enhancement and noise suppression. In this article, an effective alternative approach, fractional Fourier entropy (FrFE)-based hyperspectral anomaly detection method, is proposed. First, fractional Fourier transform (FrFT) is employed as preprocessing, which obtains features in an intermediate domain between the original reflectance spectrum and its Fourier transform with complementary strengths by space-frequency representations. It is desirable for noise removal so as to enhance the discrimination between anomalies and background. Furthermore, an FrFE-based step is developed to automatically determine an optimal fractional transform order. With a more flexible constraint, i.e., Shannon entropy uncertainty principle on FrFT, the proposed method can significantly distinguish signal from background and noise. Finally, the proposed FrFE-based anomaly detection method is implemented in the optimal fractional domain. Experimental results obtained on real hyperspectral datasets demonstrate that the proposed method is quite competitive.
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Cites
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Cited by
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
Hyperspectral anomaly detection with robust graph autoencoders
Fan, G., Ma, Y., Mei, X., Fan, F., Huang, J., & Ma, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3097097 · Cited by 212
A Novel Filter-Based Anomaly Detection Framework for Hyperspectral Imagery
Wang, W., Song, W., Li, Z., Zhao, B., & Zhao, B. (2021) · IEEE Access · doi:10.1109/access.2021.3110791 · Cited by 7
Hyperspectral anomaly detection with vision transformer and adversarial refinement
Xu, Y., Zhao, K., Zhang, L., Zhu, M., & Zeng, D. (2023) · International Journal of Remote Sensing · doi:10.1080/01431161.2023.2229495 · Cited by 6
Metadata
- Authors
- Tao, R., Zhao, X., Li, W., Li, H.-C., & Du, Q.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2019-12
- Volume
- 12
- Issue
- 12
- Pages
- 4920-4929
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2975506318
- Cited by
- 264
- References
- 32
- FWCI
- 18.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Funders
- National Natural Science Foundation of China (61421001, 61701036, U1833203)
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