Transfer learning from simulated SAR imagery using multi-output convolutional neural networks

Ali Ahmadibeni; Branndon Jones; Amir Shirkhodaie

doi:10.1117/12.2568922

31 August 2020 Transfer learning from simulated SAR imagery using multi-output convolutional neural networks

Ali Ahmadibeni, Branndon Jones, Amir Shirkhodaie

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110U (2020) https://doi.org/10.1117/12.2568922
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Deep Learning classifiers, particularly, Convolutional Neural Networks (CNNs), have been demonstrated to be very effective in the area of SAR Automatic Target Recognition (ATR). Despite of this achievement, still there is challenges with the effective multi-feature classification of spackled SAR imagery suffering from low signal-to-noise ratio. In this paper, we address technical challenges of implementing a Multi-output Convolutional Neural Network (M-CNN) as an effective multi-feature classifier model. Primarily, we employed the IRIS Electromagnetic (IRIS EM) modeling and simulation software to generate systematic simulated SAR images from an array of physics-based CAD models of 350 target vehicles scanned from different azimuth and elevation angles. For denoising and classification of image, we proposed a step-wise retraining of a CNN via a transfer learning technique. The proposed classifier achieves higher levels of model generalization on unseen data when tested against IRIS-SAR datasets. Further, we performed multi-feature classifications of target vehicles using the M-CNN model. The proposed M-CNN shows both efficiency and effectiveness in performing multi-feature classification of test target vehicles simultaneously. In this paper, we discuss our classification results while comparing its performance against those from different comparable CNN classifier models.

Conference Presentation

Citation Download Citation

Ali Ahmadibeni, Branndon Jones, and Amir Shirkhodaie "Transfer learning from simulated SAR imagery using multi-output convolutional neural networks", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110U (31 August 2020); https://doi.org/10.1117/12.2568922

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