Dimensionality reduction methods for SAR target recognition

Benjamin Lewis; Matthew Scherreik

doi:10.1117/12.2661102

13 June 2023 Dimensionality reduction methods for SAR target recognition

Benjamin Lewis, Matthew Scherreik

Proceedings Volume 12520, Algorithms for Synthetic Aperture Radar Imagery XXX; 125200M (2023) https://doi.org/10.1117/12.2661102
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Synthetic Aperture Radar is an all-weather sensor with many uses, including target recognition. We present work in train a network on synthetic SAR imagery for good performance on measured images. Previous work has used PCA decomposition to a dataset of synthetic and measured SAR imagery for image recognition with initially promising results. This work continues this line of research with kernel PCA using a number of kernels. These techniques are fit using synthetic SAR images, then the measured images are projected into the space at test time. Networks are trained on the lower dimension vectors from the synthetic images and tested on measured images. Performing dimensionality reduction in this way has applications for increased speed of network training and evaluation and in reducing the difference between synthetic and measured domains. We present the results on the publicly available SAMPLE dataset.

Conference Presentation

Citation Download Citation

Benjamin Lewis and Matthew Scherreik "Dimensionality reduction methods for SAR target recognition", Proc. SPIE 12520, Algorithms for Synthetic Aperture Radar Imagery XXX, 125200M (13 June 2023); https://doi.org/10.1117/12.2661102

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