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Optical Engineering

Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection
Author(s): Nicola Acito; Salvatore Resta; Marco Diani; Giovanni Corsini
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Paper Abstract

A novel technique for anomalous change detection (ACD) in hyperspectral images is presented. The technique embeds a strategy robust to residual misregistration errors that typically affect data collected by airborne platforms. Furthermore, the proposed technique mitigates the negative effects due to random noise, by means of a band selection technique aimed at discarding spectral channels whose useful signal content is low compared to the noise contribution. Band selection is performed on a per-pixel basis by exploiting the estimates of the noise variance accounting also for the presence of the signal-dependent noise component. Real data collected by a new generation airborne hyperspectral camera on a complex urban scenario are considered to test the proposed method. Performance evaluation shows the effectiveness of the proposed approach with respect to a previously proposed ACD algorithm based on the same similarity measure.

Paper Details

Date Published: 12 March 2013
PDF: 15 pages
Opt. Eng. 52(3) 036202 doi: 10.1117/1.OE.52.3.036202
Published in: Optical Engineering Volume 52, Issue 3
Show Author Affiliations
Nicola Acito, Accademia Navale di Livorno (Italy)
Salvatore Resta, Univ. di Pisa (Italy)
Marco Diani, Univ. di Pisa (Italy)
Giovanni Corsini, Univ. di Pisa (Italy)


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