Anomaly Detection for Longwave FLIR Imagery Using Kernel Wavelet-RX
This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) Forward Looking Infrared (FLIR) imagery. The proposed approach called kernel wavelet-RX algorithm is essentially an extension of the wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high dimensional feature space (possibly infinite) via a certain nonlinear mapping function of the input data. The wavelet-RX algorithm in this high dimensional feature space can easily be implemented in terms of kernels that implicitly compute dot products in the feature space (kernelizing the wavelet-RX algorithm). In our kernel 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 kernel RX algorithm is then applied to these subband-image cubes obtained from wavelet decomposition of the LW database images. Experimental results are presented for the proposed kernel wavelet-RX, wavelet-RX and the classical CFAR algorithm for detecting anomalies (targets) in a large database of LW imagery. The ROC plots show that the proposed kernel wavelet-RX algorithm outperforms the wavelet-RX as well as the classical CFAR detector.