Airborne SAR Autofocus Based on Blurry Imagery Classification
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 a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/3872 |
| _version_ | 1797515784858107904 |
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| author | Jianlai Chen Hanwen Yu Gang Xu Junchao Zhang Buge Liang Degui Yang |
| author_facet | Jianlai Chen Hanwen Yu Gang Xu Junchao Zhang Buge Liang Degui Yang |
| author_sort | Jianlai Chen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:53:06Z |
| format | Article |
| id | doaj.art-7f56d4fda1e64bdb8a4f939c9164fc21 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:53:06Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7f56d4fda1e64bdb8a4f939c9164fc212023-11-22T16:42:06ZengMDPI AGRemote Sensing2072-42922021-09-011319387210.3390/rs13193872Airborne SAR Autofocus Based on Blurry Imagery ClassificationJianlai Chen0Hanwen Yu1Gang Xu2Junchao Zhang3Buge Liang4Degui Yang5School of Aeronautics and Astronautics, Central South University, Changsha 410083, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information Science and Engineering, Southeast University, Nanjing 210096, ChinaSchool of Aeronautics and Astronautics, Central South University, Changsha 410083, ChinaSchool of Aeronautics and Astronautics, Central South University, Changsha 410083, ChinaSchool of Aeronautics and Astronautics, Central South University, Changsha 410083, ChinaExisting 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.https://www.mdpi.com/2072-4292/13/19/3872synthetic aperture radar (SAR)autofocusmotion compensation (MoCo)motion errordeep leaning |
| spellingShingle | Jianlai Chen Hanwen Yu Gang Xu Junchao Zhang Buge Liang Degui Yang Airborne SAR Autofocus Based on Blurry Imagery Classification Remote Sensing synthetic aperture radar (SAR) autofocus motion compensation (MoCo) motion error deep leaning |
| title | Airborne SAR Autofocus Based on Blurry Imagery Classification |
| title_full | Airborne SAR Autofocus Based on Blurry Imagery Classification |
| title_fullStr | Airborne SAR Autofocus Based on Blurry Imagery Classification |
| title_full_unstemmed | Airborne SAR Autofocus Based on Blurry Imagery Classification |
| title_short | Airborne SAR Autofocus Based on Blurry Imagery Classification |
| title_sort | airborne sar autofocus based on blurry imagery classification |
| topic | synthetic aperture radar (SAR) autofocus motion compensation (MoCo) motion error deep leaning |
| url | https://www.mdpi.com/2072-4292/13/19/3872 |
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