Synthetic aperture radar is an all-weather sensor with many uses, including target recognition. We present our latest efforts to train a network on synthetic SAR imagery for good performance on measured images. We apply an eigenimage-based classification network to the SAMPLE dataset, a dataset of synthetic and measured SAR imagery. Eigenimages are extracted from the synthetic images, then used to encode both types of images. This encoding takes the form of a vector describing the weighted contribution of each eigenimage to a given image. This reduces the extraneous noise in the measured image and helps bridge the gap between the two domains. We train a variety of networks, including fully-connected, support vector machines, and logistic regression, on the weight vectors for synthetic images, then test on measured vectors. We present the results on the publicly available SAMPLE dataset.
Machine learning systems are known to require large amounts of data to effectively generalize. When this data isn’t available, synthetically generated data is often used in its place. With synthetic aperture radar (SAR) imagery, the domain shift required to effectively transfer knowledge from simulated to measured imagery is non-trivial. We propose a pairing of convolutional networks (CNNs) with generative adversarial networks (GANs) to learn an effective mapping between the two domains. Classification networks are trained individually on measured and synthetic data, then a mapping between layers of the two CNNs is learned using a GAN.
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