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Proceedings Paper

Experimental performance analysis of hyperspectral anomaly detectors
Author(s): Nicola Acito; Giovanni Corsini; Marco Diani; Andrea Cini
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Paper Abstract

Anomaly detectors are used to reveal the presence of objects having a spectral signature that differs from the one of the surrounding background area. Since the advent of the early hyper-spectral sensors, anomaly detection has gained an ever increasing attention from the user community because it represents an interesting application both in military and civilian applications. The feature that makes anomaly detection attractive is that it does not require the difficult step of atmospheric correction which is instead needed by spectral signature based detectors to compare the received signal with the target reflectance. The aim of this paper is that of investigating different anomaly detection strategies and validating their effectiveness over a set of real hyper-spectral data. Namely, data acquired during an ad-hoc measurement campaign have been used to make a comparative analysis of the performance achieved by four anomaly detectors. The detectors considered in this analysis are denoted with the acronyms of RX-LOCAL, RX-GLOBAL, OSP-RX, and LGMRX. In the paper, we first review the statistical models used to characterize both the background and the target contributions, then we introduce the four anomaly detectors mentioned above and summarise the hypotheses under which they have been derived. Finally, we describe the methodology used for comparing the algorithm performance and present the experimental results.

Paper Details

Date Published: 10 November 2004
PDF: 11 pages
Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); doi: 10.1117/12.565858
Show Author Affiliations
Nicola Acito, Univ. di Pisa (Italy)
Giovanni Corsini, Univ. di Pisa (Italy)
Marco Diani, Univ. di Pisa (Italy)
Andrea Cini, CISAM (Italy)


Published in SPIE Proceedings Vol. 5573:
Image and Signal Processing for Remote Sensing X
Lorenzo Bruzzone, Editor(s)

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