Independent component analysis for hyperspectral anomaly

S. H. Chen; X. R. Li; C. Q. Xia; S. Q. Yu

doi:10.1117/12.2619108

31 May 2022 Independent component analysis for hyperspectral anomaly

S. H. Chen, X. R. Li, C. Q. Xia, S. Q. Yu

Proceedings Volume 12094, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII; 120940Y (2022) https://doi.org/10.1117/12.2619108
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

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.

Citation Download Citation

S. H. Chen, X. R. Li, C. Q. Xia, and S. Q. Yu "Independent component analysis for hyperspectral anomaly", Proc. SPIE 12094, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII, 120940Y (31 May 2022); https://doi.org/10.1117/12.2619108

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available