Wavelet-based hyperspectral target detection using spectral fringe-adjusted joint transform correlation

Recently, the 1-D spectral fringe-adjusted joint transform correlation (SFJTC) technique has been combined with the discrete wavelet transform (DWT) as an effective means for providing robust target detection in hyperspectral imagery. This paper expands upon earlier work that demonstrates the utility of the DWT in conjunction with SFJTC for detection. We show that using selected DWT coefficients at a given decomposition level can significantly improve the ROC curve behavior of the detection process in comparison to using the original hyperspectral signatures. The DWT coefficients that are selected for detection are based on a supervised training process that uses the pure target signature and randomly selected samples from the scene. We illustrate this by conducting experiments on two different hyperspectral scenes containing varying amounts of simulated noise. Results show that use of the selected DWT coefficients significantly improves the ROC curve detection behavior in the presence of noise.