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Airborne SAR Autofocus Based on Blurry Imagery Classification
| Content Provider | MDPI |
|---|---|
| Author | Chen, Jianlai Yu, Hanwen Xu, Gang Zhang, Junchao Liang, Buge Yang, Degui |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 3872 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13193872 |
| Journal | Remote Sensing |
| Issue Number | 19 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-27 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Synthetic Aperture Radar (sar) Autofocus Motion Compensation (moco) Motion Error Deep Leaning |
| Content Type | Text |
| Resource Type | Article |
