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Overview for Statistical Modeling of SAR Images

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Characterization of SAR Clutter and Its Applications to Land and Ocean Observations

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Abstract

Statistical modeling of SAR images is one of the basic problems of SAR image interpretation. It involves several fields such as pattern recognition, image processing, signal analysis, probability theory, and electromagnetic scattering characteristics analysis of targets etc. [1]. Generally speaking, statistical modeling of SAR images falls into the category of computer modeling and simulation. At present, one of the major strategies of SAR image interpretation is to use the methods of classical statistical pattern recognition which are based on Bayesian Theory and can reach a theoretically optimal solution [1, 2]. To utilize these methods for SAR image interpretation, a proper statistical distribution must be adopted to model SAR image data [1, 2]. Therefore, in the past ten years, statistical modeling of SAR image has become an active research field [1].

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  1. National University of Defense Technology, Changsha, China

    Gui Gao

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Gao, G. (2019). Overview for Statistical Modeling of SAR Images. In: Characterization of SAR Clutter and Its Applications to Land and Ocean Observations. Springer, Singapore. https://doi.org/10.1007/978-981-13-1020-1_1

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