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Proceedings Paper

Anomaly detection in wavelet domain for long-wave FLIR imagery
Author(s): Asif Mehmood; Nasser M. Nasrabadi
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Paper Abstract

This paper describes a new wavelet-based anomaly detection technique for Forward Looking Infrared (FLIR) sensor consisting a Long-wave (LW) and a Mid-wave (MW) sensor. The proposed approach called wavelet-RX algorithm consists of a combination of a two-dimensional (2-D) wavelet transform and the well-known multivariate anomaly detector called the RX algorithm. In our wavelet-RX algorithm, a 2-D wavelet transform is first applied to decompose the input image into uniform subbands. A number of significant subbands (high energy subbands) are concatenated together to form a subband-image cube. The RX algorithm is then applied to each subbandimage cube obtained from wavelet decomposition of LW and MW sensor data separately. Experimental results are presented for the proposed wavelet-RX and the classical CFAR algorithm for detecting anomalies (targets) in a single broadband FLIR (LW or MW) sensors. The results show that the proposed wavelet-RX algorithm outperforms the classical CFAR detector for both LW and for MW FLIR sensors data.

Paper Details

Date Published: 12 May 2010
PDF: 11 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960S (12 May 2010); doi: 10.1117/12.850211
Show Author Affiliations
Asif Mehmood, U.S. Army Research Lab. (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; David P. Casasent; Steven L. Chodos; Abhijit Mahalanobis; William E. Thompson; Tien-Hsin Chao, Editor(s)

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