Generalized matched filters and univariate Neyman-Pearson detectors for image target detection

I derive two-stage, statistically suboptimal target detectors for images. The first, or transformation, stage is a "generalized matched filter" (GMF) that linearly transforms the input image. I propose three rational signal-to-noise-ratio criteria whose maximization yields the three GMFs. The second, or detection, stage is a univariate "Neyman-Pearson detector" (NPD), which executes a pointwise likelihood ratio test on the GMF transformed images. Experiments on infrared and synthetic-aperture radar imagery compare GMF/NPDs with several established detectors.