In this paper, an innovative approach to microwave imaging that combines qualitative imaging and deep learning is presented. The goal is to set a framework for a reliable and user-independent retrieval of the shapes of unknown targets. To this end, the proposed approach exploits an inversion technique known as orthogonality sampling method, which is capable of providing a qualitative estimation of the shape of targets in realtime. The output of the qualitive inversion is processed by a deep learning fully convolutional network called U-Net. U-Net automatically generates binary masks depicting the geometrical properties of the targets, i.e., separates the scattering objects (foreground) from the background. A quantitative assessment of the performance of the processing framework is provided with simulated data to demonstrate the capabilities of the proposed approach.
Deep learning-enhanced qualitative microwave Imaging. Rationale and Initial assessment / Yago, A.; Cavagnaro, M.; Crocco, L.. - (2021), pp. 1-5. (Intervento presentato al convegno 15th European Conference on Antennas and Propagation, EuCAP 2021 tenutosi a Dusseldorf, Germany) [10.23919/EuCAP51087.2021.9411361].
Deep learning-enhanced qualitative microwave Imaging. Rationale and Initial assessment
Cavagnaro M.;
2021
Abstract
In this paper, an innovative approach to microwave imaging that combines qualitative imaging and deep learning is presented. The goal is to set a framework for a reliable and user-independent retrieval of the shapes of unknown targets. To this end, the proposed approach exploits an inversion technique known as orthogonality sampling method, which is capable of providing a qualitative estimation of the shape of targets in realtime. The output of the qualitive inversion is processed by a deep learning fully convolutional network called U-Net. U-Net automatically generates binary masks depicting the geometrical properties of the targets, i.e., separates the scattering objects (foreground) from the background. A quantitative assessment of the performance of the processing framework is provided with simulated data to demonstrate the capabilities of the proposed approach.File | Dimensione | Formato | |
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