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Shaoqi Yu

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Proceedings Article | 31 May 2022 Poster + Paper

Independent component analysis for hyperspectral anomaly

S. Chen, X. Li, C. Xia, S. Yu

Proceedings Volume 12094, 120940Y (2022) https://doi.org/10.1117/12.2619108

KEYWORDS: Independent component analysis, Target detection, Sensors, Data modeling, Hyperspectral target detection, 3D image processing

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This paper develops an independent component analysis (ICA) with subspace projection for hyperspectral anomaly detection. The proposed model represents the sphered data set X as an ICA model specified by a two-component orthogonal sum decomposition, X IC N = +j with j independent components, j IC , generated by ICA and a noise component N. To better extract anomalies from the j IC component space, the concept of sparsity cardinality (SC) is integrated into ICA to derive a ICASC anomaly detector (ICASC-AD). For determine appropriate values of j, the virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD) are used for this purpose. The experimental results demonstrate that ICASC are very competitive against the LRaSR-based models in hyperspectral anomaly detection.

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