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.
|Autori interni:||DIANI, MARCO|
|Autori:||Acito N; Resta S; Diani M; Corsini G|
|Titolo:||Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection|
|Anno del prodotto:||2013|
|Digital Object Identifier (DOI):||10.1117/1.OE.52.3.036202|
|Appare nelle tipologie:||1.1 Articolo in rivista|