Investigating the saliency of SAR image chips

Benjamin Lewis; Theresa Scarnati; Ernest Parke

doi:10.1117/12.2558364

24 April 2020 Investigating the saliency of SAR image chips

Benjamin Lewis, Theresa Scarnati, Ernest Parke

Proceedings Volume 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII; 113930K (2020) https://doi.org/10.1117/12.2558364
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Machine learning techniques such as convolutional neural networks have progressed rapidly in the past few years, propelled by their rampant success in many areas. Convolutional networks work by transforming input images into compact representations that cluster well with the representations of related images. However, these representations are often not human-interpretable, which is unsatisfying. One field of research, image saliency, attempts to show where in an image a trained network is looking to obtain its information. With this method, well-trained networks will reveal a focus on the object matching the label and ignore the background or other objects. We train and test neural networks on synthetic SAR imagery and use image saliency techniques to investigate the areas of the image on which the network is focused. Doing so should reveal whether the network is using relevant information in the image, such as the shape of the target. We test various image saliency techniques and classification networks, then measure and comment on the resulting saliency results to gain insight into what the networks learn on simulated SAR data. This investigation is designed to serve as a tool for evaluating future SAR target recognition machine learning algorithms.

Conference Presentation

Citation Download Citation

Benjamin Lewis, Theresa Scarnati, and Ernest Parke "Investigating the saliency of SAR image chips", Proc. SPIE 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII, 113930K (24 April 2020); https://doi.org/10.1117/12.2558364

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