Abstract
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In this paper, an ensemble learning-based autofocus method is proposed. Convolutional Extreme Learning Machine (CELM) is constructed and utilized to estimate the phase error. However, the performance of a single CELM is poor. To overcome this, a novel, metric-based combination strategy is proposed, combining multiple CELMs to further improve the estimation accuracy. The proposed model is trained with the classical bagging-based ensemble learning method. The training and testing process is non-iterative and fast. Experimental results conducted on real SAR data show that the proposed method has a good trade-off between focusing quality and speed.
Highlights
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data
We focus on the development of a non-iterative autofocus algorithm based on machine learning
We propose a machine-learning-based SAR autofocus algorithm
Summary
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. The imaging quality of SAR is usually degraded by undesired Phase Errors (PEs). For high-quality imaging, especially high-resolution imaging, it is important to compensate for these PEs. Autofocus is a data-driven technique, which can directly estimate the phase error from the backscattered signals [5]. These methods can be classified into the following three categories: sub-aperture-based, inverse-filteringbased, and metric-optimization-based algorithms. The sub-aperture autofocus algorithm is called Map Drift Autofocus (MDA) [6]. The more sub-apertures that are divided, the higher the order of phase error that can be estimated [8]. The sub-aperture-based algorithms cannot be used to correct high-order phase errors, which are limited by the number of sub-apertures. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
