Synergistic fusion of wavelet and superpixels for complementary hyperspectral anomaly detection
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03238-6.pdf
Reference27 articles.
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2. Hu, X., Xie, C., Fan, Z., Duan, Q., Zhang, D., Jiang, L., Wei, X., Hong, D., Li, G., Zeng, X., Chen, W., Wu, D., Chanussot, J.: Hyperspectral anomaly detection using deep learning: a review. Remote Sens. 14, 1973 (2022). https://doi.org/10.3390/rs14091973
3. Reed, I.S., Yu, X.: Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 38(10), 1760–1770 (1990). https://doi.org/10.1109/29.60107
4. He, F., Yan, S., Ding, Y., Sun, Z., Zhao, J., Hu, H., Zhu, Y.: Recursive RX with extended multi-attribute profiles for hyperspectral anomaly detection. Remote Sens. 15, 589 (2023). https://doi.org/10.3390/rs15030589
5. Borghys, D., Kåsen, I., Achard, V., Perneel, C.: Comparative evaluation of hyperspectral anomaly detectors in different types of background. In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. pp. 803–814. SPIE (2012). https://doi.org/10.1117/12.920387
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