http://www.cnr.it/ontology/cnr/individuo/prodotto/ID251419

LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas (Contributo in atti di convegno)

Type

Prodotto della ricerca (Classe)
Contributo in atti di convegno (Classe)

Label

LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas (Contributo in atti di convegno) (literal)

Anno

2013-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1117/12.2015761 (literal)

Alternative label

Dekker, R.J.a , Schwering, P.B.W.a, Benoist, K.W.a, Pignatti, S.b, Santini, F.b, Friman, O.c (2013)
LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas
in SPIE Proceedings - Volume 8743 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, Baltimore, MD; United States;, 29 April 2013 through 2 May 2013;
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Dekker, R.J.a , Schwering, P.B.W.a, Benoist, K.W.a, Pignatti, S.b, Santini, F.b, Friman, O.c (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://dx.doi.org/10.1117/12.2015761 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874306 (May 18, 2013) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

8743 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

9 (literal)

Note

ISI Web of Science (WOS) (literal)
Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

a TNO, PO Box 96864, 2509 JG The Hague, Netherlands b CNR IMAA, Zona Industriale, 85050 Tito Scalo (PZ), Italy c FOI, PO Box 1165, 58111 Linköping, Sweden (literal)

Titolo

LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

978-0-8194-9534-1 (literal)

Abstract

This paper studies change detection of LWIR (Long Wave Infrared) hyperspectral imagery. Goal is to improve target acquisition and situation awareness in urban areas with respect to conventional techniques. Hyperspectral and conventional broadband high-spatial-resolution data were collected during the DUCAS trials in Zeebrugge, Belgium, in June 2011. LWIR data were acquired using the ITRES Thermal Airborne Spectrographic Imager TASI-600 that operates in the spectral range of 8.0-11.5 ?m (32 band configuration). Broadband data were acquired using two aeroplanemounted FLIR SC7000 MWIR cameras. Acquisition of the images was around noon. To limit the number of false alarms due to atmospheric changes, the time interval between the images is less than 2 hours. Local co-registration adjustment was applied to compensate for misregistration errors in the order of a few pixels. The targets in the data that will be analysed in this paper are different kinds of vehicles. Change detection algorithms that were applied and evaluated are Euclidean distance, Mahalanobis distance, Chronochrome (CC), Covariance Equalisation (CE), and Hyperbolic Anomalous Change Detection (HACD). Based on Receiver Operating Characteristics (ROC) we conclude that LWIR hyperspectral has an advantage over MWIR broadband change detection. The best hyperspectral detector is HACD because it is most robust to noise. MWIR high spatial-resolution broadband results show that it helps to apply a false alarm reduction strategy based on spatial processing. (literal)

Editore