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14 May 2019 A deep learning approach to the Synthetic and Measured Paired and Labeled Experiment (SAMPLE) challenge problem
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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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Theresa Scarnati and Benjamin Lewis "A deep learning approach to the Synthetic and Measured Paired and Labeled Experiment (SAMPLE) challenge problem", Proc. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, 109870G (14 May 2019); https://doi.org/10.1117/12.2523458
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