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12 May 2010 Anomaly detection in wavelet domain for long-wave FLIR imagery
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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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Asif Mehmood and Nasser M. Nasrabadi "Anomaly detection in wavelet domain for long-wave FLIR imagery", Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960S (12 May 2010);

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