Airborne SAR Autofocus Based on Blurry Imagery Classification

Autor: Jianlai Chen, Hanwen Yu, Gang Xu, Junchao Zhang, Buge Liang, Degui Yang
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Remote Sensing, Vol 13, Iss 19, p 3872 (2021)
Druh dokumentu: article
ISSN: 13193872
2072-4292
DOI: 10.3390/rs13193872
Popis: 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.
Databáze: Directory of Open Access Journals
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