Hyperspectral anomaly detection with nonlocal self-similarity prior

Yulin Yao; Hongyi Liu; Jun Zhang

doi:10.1117/12.2600404

1 June 2021 Hyperspectral anomaly detection with nonlocal self-similarity prior

Yulin Yao, Hongyi Liu, Jun Zhang

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 118480I (2021) https://doi.org/10.1117/12.2600404
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

In hyperspectral image, the variation of endmember may significantly alter the signature of corresponding endmember, which influences the detection of anomaly target. In order to distinguish the endmember variability and outlier effectively, a Bayesian anomaly detection being considered the endmember variability unmixing is proposed. The parameters priors are built according to the perturbed linear mixing model. At the same time, outliers usually have high correlations in the spatial domain. So as background. Moreover, the anomaly prior is developed by combining the nonlocal self-similarity and Markov random field priors for a Boolean label map which takes the spatial correlations of the image into consideration. Compared with some classical anomaly detection methods, the experiments on datasets show that the proposed method can effectively improve the detection accuracy and enhance the visual effect.

Citation Download Citation

Yulin Yao, Hongyi Liu, and Jun Zhang "Hyperspectral anomaly detection with nonlocal self-similarity prior", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 118480I (1 June 2021); https://doi.org/10.1117/12.2600404

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