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.
|