Abstract
A supervised subpixel target detection algorithm based on iterative simple linear model for hyperspectral imaging is developed. Parameter estimation, whitening transformation, and comparison of the test results with the classical approach are discussed. Numerical results indicate that the performance of the parametric algorithm is comparable with the corresponding classical approach and requires fewer training pixels.
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