A Deep Learning Solution for Phase Screen Estimation in SAR Tomography

Hossein Aghababaei

doi:10.1109/LGRS.2025.3555441

A Deep Learning Solution for Phase Screen Estimation in SAR Tomography

2025-01-01

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DOI：10.1109/LGRS.2025.3555441

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Multibaseline and tomographic synthetic aperture radar (SAR) data are often affected by phase distortions known as phase screens. These distortions stem either from atmospheric effects or residual errors in platform motion. Calibrating and compensating for the phase screen is crucial to prevent spreading and defocusing in multidimensional tomographic imaging. Given the growing interest in artificial intelligence and deep learning, we aim to utilize their potential to develop a phase calibration process for SAR tomographic data. Our proposed framework is based upon a convolutional neural network (CNN) and generates training patches directly from the tomographic images under consideration, without relying on external references or resources. Once trained, the network effectively estimates phase distortions across the entire image; these are then used to calibrate the tomographic data. Experimental results from AfriSAR and UAVSAR tomographic datasets are included to showcase the effectiveness of the proposed solution.

Keyword:

Training

Tomography

Phase distortion

Calibration

Training data

Synthetic aperture radar

Atmospheric modeling

Vectors

Deep learning

Data models

Convolutional neural network (CNN)

deep learning

phase calibration

phase screen

synthetic aperture radar (SAR) tomography (TomoSAR)

A Deep Learning Solution for Phase Screen Estimation in SAR Tomography

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