A Hyperspectral Anomaly Detection Algorithm Based on Non-local Self-Similarity

WANG Yang; LIU Zhigang; JU Huihui; WANG Yiting

doi:10.3969/j.issn.1671-637x.2020.05.009

Journals >Electronics Optics & Control >Volume 27 >Issue 5 >Page 42 > Article

Electronics Optics & Control
Vol. 27, Issue 5, 42 (2020)

A Hyperspectral Anomaly Detection Algorithm Based on Non-local Self-Similarity

WANG Yang¹, LIU Zhigang¹, JU Huihui², and WANG Yiting¹

Author Affiliations

¹[in Chinese]

²[in Chinese]

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DOI: 10.3969/j.issn.1671-637x.2020.05.009 Cite this Article

WANG Yang, LIU Zhigang, JU Huihui, WANG Yiting. A Hyperspectral Anomaly Detection Algorithm Based on Non-local Self-Similarity[J]. Electronics Optics & Control, 2020, 27(5): 42 Copy Citation Text

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References

[3] LI W, DU Q.Collaborative representation for hyperspectral anomaly detection［J］.IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(3):1463-1474.

[4] SUN W W, LIU C, LI J L, et al.Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery［J］.Journal of Applied Remote Sensing, 2014, 8(1):083641.

[8] SALMON J.On two parameters for denoising with non-local means［J］.IEEE Signal Processing Letters, 2010, 17(3):269-272.

[9] BUADES A, COLL B, MOREL J M.Nonlocal image and movie denoising［J］.International Journal of Computer Vision, 2008, 76(2):123-139.

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[1] MENG Lei. Multi-agent Leader-Follower Formation Control Based on Distance Constraints[J]. Electronics Optics & Control, 2021, 28(7): 48

WANG Yang, LIU Zhigang, JU Huihui, WANG Yiting. A Hyperspectral Anomaly Detection Algorithm Based on Non-local Self-Similarity[J]. Electronics Optics & Control, 2020, 27(5): 42

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