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

Rob J. Dekker; Piet B. W. Schwering; Koen W. Benoist; Stefano Pignatti; Federico Santini; Ola Friman

doi:10.1117/12.2015761

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18 May 2013 LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas

Rob J. Dekker, Piet B. W. Schwering, Koen W. Benoist, Stefano Pignatti, Federico Santini, Ola Friman

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Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 874306 (2013) https://doi.org/10.1117/12.2015761
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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 μm (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.

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Rob J. Dekker, Piet B. W. Schwering, Koen W. Benoist, Stefano Pignatti, Federico Santini, and Ola Friman "LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874306 (18 May 2013); https://doi.org/10.1117/12.2015761

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