7 May 2007 A theoretical framework for hyperspectral anomaly detection using spectral and spatial a priori information
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Abstract
This study presents a new theoretical approach for anomaly detection using a priori information about targets. This a priori knowledge deals with the general spectral behavior and the spatial distribution of targets. In this study, we consider subpixel and isolated targets which are spectrally anomalous in one region of the spectrum but not in another. This method is totally different from matched filters which suffer from a relative sensitivity to low errors in the target spectral signature. We incorporate the spectral a priori knowledge in a new detection distance and we propose a Bayesian approach with a markovian regularization to suppress the potential targets that do not respect the spatial a priori. The interest of the method is illustrated on simulated data consisting in realistic anomalies superimposed on a real HyMap hyperspectral image.
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Brice Yver, Rodolphe Marion, "A theoretical framework for hyperspectral anomaly detection using spectral and spatial a priori information", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656507 (7 May 2007); doi: 10.1117/12.718601; https://doi.org/10.1117/12.718601
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KEYWORDS
Target detection

Vegetation

Hyperspectral imaging

Hyperspectral target detection

Detection and tracking algorithms

Binary data

Computer simulations

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