Abstract
Existing airborne SAR autofocus methods can be classified as parametric and non-parametric. Generally, non-parametric methods, such as the widely used phase gradient autofocus (PGA) algorithm, are only suitable for scenes with many dominant point targets, while the parametric ones are suitable for all types of scenes, in theory, but their efficiency is generally low. In practice, whether many dominant point targets are present in the scene is usually unknown, so determining what kind of algorithm should be selected is not straightforward. To solve this issue, this article proposes an airborne SAR autofocus approach combined with blurry imagery classification to improve the autofocus efficiency for ensuring autofocus precision. In this approach, we embed the blurry imagery classification based on a typical VGGNet in a deep learning community into the traditional autofocus framework as a preprocessing step before autofocus processing to analyze whether dominant point targets are present in the scene. If many dominant point targets are present in the scene, the non-parametric method is used for autofocus processing. Otherwise, the parametric one is adopted. Therefore, the advantage of the proposed approach is the automatic batch processing of all kinds of airborne measured data.
Highlights
This paper proposes an airborne synthetic aperture radar (SAR) autofocus approach combined with blurry imagery classification
A SAR autofocus approach based on blurry imagery classification is proposed
The blurry imagery classification is based on a typical VGGNet in a deep learning community
Summary
Different from the spaceborne synthetic aperture radar (SAR) [1,2,3,4,5,6], airborne SAR is frequently affected by atmospheric turbulence, and its flight trajectory may deviate from a pre-planned straight-line trajectory [7,8,9,10]. Combining motion compensation (MoCo)/autofocus processing for airborne SAR imaging [11,12,13,14] is necessary. The motion compensation technique combined with the inertial navigation system (INS) and/or global position system (GPS) data cannot meet the expected accuracy requirements because the aircraft may not be able to carry enough high-precision INS/GPS equipment [15,16,17]. The autofocus technique based on radar raw data needs to be implemented in airborne SAR imaging
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