Study on missile-borne SAR imaging quality assessment based on global features

Abstract

Synthetic aperture radar (SAR) image quality assessment can provide a measure of the strengths and weaknesses of SAR imaging algorithms, which provides an important reference for the selection of SAR imaging algorithms and how to improve SAR image quality. In this paper, some commonly used SAR image quality indicators based on global features are firstly selected, followed by the introduction of texture feature-based quality indicators and image gradient- based quality indicators to enrich the SAR image quality indicators based on global features, and then these indicators are used to comprehensively assess the quality of SAR images obtained from the same measured data processed by two different missile-borne SAR imaging algorithms. This paper also investigates the problem of how to improve SAR image quality by using the phase gradient autofocus algorithm (PGA) to improve a missile-borne SAR imaging algorithm and investigates the effect of the PGA algorithm on the focus quality of missile-borne SAR images using the image quality assessment indicators proposed in this paper. The simulation results show that the selected SAR image quality assessment indicators can accurately and comprehensively assess the image quality obtained by the missile- borne SAR imaging algorithm, and the PGA algorithm is an effective method to improve the quality of missile-borne SAR images.