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21 September 2007 Target detection in hyperspectral imagery using one-dimensional extended maximum average correlation height filter and mahalanobis distance
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Abstract
Target detection in hyperspectral imagery is a challenging task as the targets occupy only a few pixels or less. The presence of noise can make detection more complicated as spectral signature of pixels can change due to noise. In this paper, a novel technique for detection is proposed using one dimensional maximum average correlation height (MACH) filter. The MACH filter is trained using likely variations of target spectral signatures. The variations can be taken from data or can be generated by applying Gaussian noise. Each pixels of the input scene is then correlated with the detection filter. The MACH filter maximizes the relative height of correlation peak for target in comparison with background and noise. Thus, a target can be detected by analyzing the correlation peak values. Single or Multiple targets in a hyperspectral sequence can be detected simultaneously this approach. Test results using real life hyperspectral data are presented to verify the accomplishments of one dimensional MACH filter.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. F. Islam and M. S. Alam "Target detection in hyperspectral imagery using one-dimensional extended maximum average correlation height filter and mahalanobis distance", Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 669903 (21 September 2007); https://doi.org/10.1117/12.736365
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