1 April 1999 Multi-algorithm solution for automated multispectral target detection
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Optical Engineering, 38(4), (1999). doi:10.1117/1.602115
Abstract
A solution to the problem of automated detection of targets with unknown spectral properties in multispectral imagery is presented that makes use of three background characterization and suppression algorithms in series. The first, parametric Bayesian clustering, is used to accurately characterize individual elements of the background scene. The second, background suppression filtering, eliminates those dimensions of multispectral space containing the majority of background energy. Finally, a multidimensional extension of the well-known Linde- Buzo-Gray (LBG) clustering algorithm is used to characterize what remains of the background and extract any anomalous target signatures. The results of this process are compared to spectral decorrelation (RX) filtering alone, LBG clustering alone, and RX filtering in combination with background suppression filtering. The process presented is shown to be significantly superior to each of these algorithm combinations.
Edward A. Ashton, "Multi-algorithm solution for automated multispectral target detection," Optical Engineering 38(4), (1 April 1999). http://dx.doi.org/10.1117/1.602115
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KEYWORDS
Target detection

Detection and tracking algorithms

Optical filters

Vegetation

Data modeling

Sensors

Chemical elements

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