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- W2039204920 abstract "An important application in remote sensing using hyper-spectral imaging system is the detection of anomalies in a large background. An anomaly detector for hyperspectral imagery is developed by partialling out the effect of the clutter subspace by predicting the background using a linear combination of the clutter subspace. The coefficients of the linear combination are chosen to maximize a criterion based on squared correlation. The dimension of the clutter subspace for each spectral component of the background can vary from one spectral component to another. The experimental results from a hyperspectral data cube show that the anomaly detector has a better performance than the SSRX detector." @default.
- W2039204920 created "2016-06-24" @default.
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- W2039204920 date "2010-06-01" @default.
- W2039204920 modified "2023-09-26" @default.
- W2039204920 title "Hyperspectral anomaly detector based on variable number of linear predictors" @default.
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- W2039204920 doi "https://doi.org/10.1109/whispers.2010.5594945" @default.
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