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A deep learning approach to the Synthetic and Measured Paired and Labeled Experiment (SAMPLE) challenge problem
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Paper Abstract

Convolutional neural networks (CNN) are tremendously successful at classifying objects in electro-optical images. However, with synthetic aperture radar (SAR) data, off-the-shelf classifiers are insufficient because there are limited measured SAR data available and SAR images are not invariant to object manipulations. In this paper, we utilize the Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset to present an approach to the SAR measured and synthetic domain mismatch problem. We pre-process the synthetic and measured data using Variance-Based Joint Sparsity despeckling, quantization, and clutter transfer techniques. The t-SNE (stochastic neighborhood embedding) dimensionality reduction method is used to show that pre-processing the data in the proposed way brings the two-dimensional manifolds represented by the measured and synthetic data closer. A DenseNet classification network is trained with unprocessed and processed data, showing that when no measured data are available for training, it is beneficial to pre-process SAR data with the proposed technique.

Paper Details

Date Published: 14 May 2019
PDF: 10 pages
Proc. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, 109870G (14 May 2019); doi: 10.1117/12.2523458
Show Author Affiliations
Theresa Scarnati, Air Force Research Lab. (United States)
Benjamin Lewis, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10987:
Algorithms for Synthetic Aperture Radar Imagery XXVI
Edmund Zelnio; Frederick D. Garber, Editor(s)

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