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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.
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Training deep learning models requires a large amount of labeled data. Acquiring accurate labels is a time-consuming and often costly effort. Active learning can make the labeling process more efficient by using feedback from the model being trained to determine the most helpful samples to label. By selecting the "most helpful" samples model performancy may be improved and the amount of training data required to reach a fixed degree of performance may be substantially reduced. Results are presented for classifying targets in synthetic aperture radar (SAR) imagery using the core set active learning method applied to the MSTAR dataset.
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This conference presentation was prepared for SPIE Defense + Commercial Sensing, 2023.
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