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Machine Learning Approaches for Data Limited SAR Object Recognition

The United States Air Force Research Laboratory

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In this lecture, we present machine learning approaches to classify data limited SAR imagery. Recently, active learning algorithms have been applied to video imagery labeling and classification. Active learning enables labeling a small subset of imagery by hand/human, learning features of an object from the labeled imagery, and then applying learned features to automatically label/classify unlabeled/new imagery.

The goal here is that instead of using lots of data (~70%) for training, can we use only a fraction (10-15%) of the data for training and thus reduce training time and learn new objects much faster.  Similar algorithms named “few-shot learning (FSL)” or “low-shot learning (LSL) also enable learning objects from a few samples of training data. We will present active learning and few-shot learning algorithms to classify SAR imagery.