Deep learning for three dimensional SAR imaging using sparse apertures

Nithin Sugavanam; Emre Ertin; Jan Rainer Jamora

doi:10.1117/12.2663716

15 June 2023 Deep learning for three dimensional SAR imaging using sparse apertures

Nithin Sugavanam, Emre Ertin, Jan Rainer Jamora

Author Affiliations +

Proceedings Volume PC12520, Algorithms for Synthetic Aperture Radar Imagery XXX; PC1252001 (2023) https://doi.org/10.1117/12.2663716
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Synthetic aperture radar (SAR) collects samples of the 3D Spatial Fourier transform on a two dimensional manifold corresponding to the backscatter data of wideband pulses launched from different look angles along an aperture. Traditional 3D reconstruction techniques involve aggregating and indexing phase history data in the spatial Fourier domain collected through set of sparse apertures and applying an inverse 3D Fourier Transform. We present a coordinate-based multi-layer perceptron (MLP) that enforces the smooth surface prior. The 3D geometry is represented using the signed distance function. Since estimating a smooth surface from a sparse and noisy point cloud is an ill-posed problem, in this work, we regularize the surface estimation by sampling points from the implicit surface representation during the training step.We validate the model's reconstruction ability using the Civilian vehicles data domes.

Conference Presentation

Citation Download Citation

Nithin Sugavanam, Emre Ertin, and Jan Rainer Jamora "Deep learning for three dimensional SAR imaging using sparse apertures", Proc. SPIE PC12520, Algorithms for Synthetic Aperture Radar Imagery XXX, PC1252001 (15 June 2023); https://doi.org/10.1117/12.2663716

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