Eigenimage-based synthetic aperture radar ATR

Benjamin Lewis; Jonathan Garrett

doi:10.1117/12.2618948

31 May 2022 Eigenimage-based synthetic aperture radar ATR

Benjamin Lewis, Jonathan Garrett

Proceedings Volume 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX; 1209508 (2022) https://doi.org/10.1117/12.2618948
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Benjamin Lewis and Jonathan Garrett "Eigenimage-based synthetic aperture radar ATR", Proc. SPIE 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX, 1209508 (31 May 2022); https://doi.org/10.1117/12.2618948

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