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- W2976666789 abstract "Classical least squares (CLS) models are flexible, adaptable, and interpretable. Four forms of CLS are presented, and the mathematical relationships are reduced to showing differences in the weighting in the corresponding objective functions. The generalized least squares (GLS) weighting strategy is demonstrated using a Raman image of a tile from the space shuttle, and a second example shows how GLS and extended least squares (ELS) can be used synergistically to detect melamine particles in wheat gluten in a near-infrared image. A final example shows how GLS-ELS can be used to classify pixels in a Landsat image. In general, de-weighting of clutter signal using GLS improved sensitivity, and selectivity was enhanced using ELS—especially when targets were similar. A distinct advantage of these target detection approaches is that they are image specific. This means that image-to-image differences in sampling and interferences need not be accounted for." @default.
- W2976666789 created "2019-10-03" @default.
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- W2976666789 date "2019-01-01" @default.
- W2976666789 modified "2023-09-24" @default.
- W2976666789 title "Classical least squares for detection and classification" @default.
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- W2976666789 doi "https://doi.org/10.1016/b978-0-444-63977-6.00011-0" @default.
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