Principal Component Reconstruction Error for Hyperspectral Anomaly Detection

Authors

James A. Jablonski, Air Force Institute of Technology
Trevor J. Bihl, Air Force Institute of Technology
Kenneth W. Bauer, Air Force Institute of Technology

Document Type

Article

Publication Date

4-30-2015

Abstract

Excerpt: In this letter, a reliable, simple, and intuitive approach for hyperspectral imagery (HSI) anomaly detection (AD) is presented. This method, namely, the global iterative principal component analysis (PCA) reconstruction-error-based anomaly detector (GIPREBAD), examines AD by computing errors (residuals) associated with reconstructing the original image using PCA projections.

Comments

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DOI

10.1109/LGRS.2015.2421813

Source Publication

IEEE Geoscience and Remote Sensing Letters (ISSN 1545-598X | eISSN 1558-0571)

Recommended Citation

J. A. Jablonski, T. J. Bihl and K. W. Bauer, "Principal Component Reconstruction Error for Hyperspectral Anomaly Detection," in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 8, pp. 1725-1729, Aug. 2015, doi: 10.1109/LGRS.2015.2421813.