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Remote Sensing

Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection

[+] Author Affiliations
Nicola Acito

Accademia Navale di Livorno, Dipartimento Armi Navali, Viale Italia 72, 57100 Livorno, Italia

Salvatore Resta

Università di Pisa, Dipartimento di Ingegneria dell’Informazione, Via Caruso 16, 56122 Pisa, Italia

Marco Diani

Università di Pisa, Dipartimento di Ingegneria dell’Informazione, Via Caruso 16, 56122 Pisa, Italia

Giovanni Corsini

Università di Pisa, Dipartimento di Ingegneria dell’Informazione, Via Caruso 16, 56122 Pisa, Italia

Opt. Eng. 52(3), 036202 (Mar 12, 2013). doi:10.1117/1.OE.52.3.036202
History: Received November 21, 2012; Revised January 28, 2013; Accepted February 20, 2013
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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.

© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Nicola Acito ; Salvatore Resta ; Marco Diani and Giovanni Corsini
"Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection", Opt. Eng. 52(3), 036202 (Mar 12, 2013). ; http://dx.doi.org/10.1117/1.OE.52.3.036202


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