Mr. Harris Hardiman-Mostow Profile

Harris Hardiman-Mostow

at Univ of California Los Angeles

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Proceedings Article | 13 June 2023 Presentation + Paper

Deep semi-supervised label propagation for SAR image classification

Joshua Enwright, Harris Hardiman-Mostow, Jeff Calder, Andrea Bertozzi

Proceedings Volume 12520, 125200G (2023) https://doi.org/10.1117/12.2663665

KEYWORDS: Machine learning, Synthetic aperture radar, Neural networks, Matrices, Feature extraction, Deep learning, Network architectures, Active learning, Image classification

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Automatic target recognition with synthetic aperture radar (SAR) data is a challenging problem due to the complexity of the images and the difficulty in acquiring labels. Recent work1 used a convolutional variational autoencoder to extract relevant features prior to constructing a similarity graph in a graph-based active learning framework for SAR data. In this work we present two novel methods for classifying SAR data that use convolutional neural network (CNN) feature extraction together with techniques from graph-based semi-supervised learning in an end-to-end manner that can provide improved classification performance in the small labeled dataset regimes that are common in SAR ATR. First, we introduce Laplace Output Activation Neural Networks (LOAN Networks) as a way of directly optimizing feature embeddings for use with graph-based semi-supervised learning techniques. Next, we introduce Pseudo Label Propagation Neural Networks (PsLaPN Networks) as a inexpensive way to both boost the training signal as well as combat overconfidence and poor model calibration in neural networks. We present a novel derivation of simple formulas for the direct and efficient computation of derivatives of the outputs of graph-based algorithms like label propagation2 for use in the training of our networks. We test the proposed end-to-end networks for active learning on OpenSARShip, a SAR dataset, where both methods surpass the previous state-of-the-art.

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