This paper is a preliminary report on investigations into the effects of data reduction on inverse synthetic aperture radar (ISAR) image quality and classifier performance. The case considered here is the effect of decimating the ISAR radar returns. Decimation of the type described induces image aliasing because of the signal processing involved in ISAR image formation. This familiar effect is used to model the degradation of object boundaries, which are used in classification. Constraints on subsampling will be derived as a function of target size, image signal-to-noise ratio, and classifier-required image quality. We will proceed in three main steps. The structure of a typical ISAR imaging and target classification system will be reviewed and the need for data reduction analyzed. We will then define a measure of image quality and its effect on classifier performance. The question will then be posed, and answered, as to the effect on image quality of decimation of the ISAR target spectral sequence (TSS). The technique used is to analyze the relationship between ISAR imaging and the two-dimensional Fourier transform, then to apply the sampling theorem from 2-D signal processing.
Allen L. Gorin, Allen L. Gorin,
"Effect Of Data Reduction On Inverse Synthetic Aperture Radar (ISAR) Image Quality And Target Shape Descriptions," Optical Engineering 21(5), 215858 (1 October 1982). https://doi.org/10.1117/12.7972994